Readable research linked to original sources
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Browse normalized, publication-ready research with direct links to its evidence and source.
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Readable research linked to original sources
Browse normalized, publication-ready research with direct links to its evidence and source.
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Readable research linked to original sources
Browse normalized, publication-ready research with direct links to its evidence and source.
158 articles
Newest firstObjective This study aimed to construct a high-efficiency dual-modal diagnostic model for treatment-resistant depression (TRD) by integrating serum metabolomics and clinical risk factors, and explore its metabolic pathological mechanisms. Methods A total of 93 major depressive disorder (MDD) patients (53 TRD, 40 non-TRD) were enrolled for a single-center retrospective study. Serum untargeted metabolomics and clini…
Open article record in new tab ↗Background Predicting progression from substance use to substance use disorder (SUD) is challenging, particularly for participants with cannabis and stimulant use who follow distinct risk trajectories. Machine learning enables integration of demographic, behavioral, wearable-derived, and social determinants of health (SDoH) data, yet few studies have compared linear and non-linear approaches in large, diverse popu…
Open article record in new tab ↗Introduction Binding affinity and functional potency are two distinct but pharmacologically related properties in ligand-receptor interactions. The molecular features influencing these two endpoints may differ, reflecting distinct physicochemical and conformational requirements for receptor binding and activation. Consequently, understanding the molecular features that determine both endpoints is essential, especi…
Introduction Recent studies show that young females now report higher e-cigarette use than males, reversing prior trends. While sex differences in use are documented, little is known about underlying risk profiles. This study applied a machine learning (ML) approach to identify and compare predictors of adolescent e-cigarette use by sex. Methods We analyzed cross-sectional data from 1829 9th graders in Southern Ca…
Open article record in new tab ↗The human maternal-fetal interface is characterized by mosaic intermingling of maternal and fetal cells1. Yet the underlying cellular, molecular and spatial programmes remain incompletely defined. Here we generate a comprehensive atlas of the human maternal-fetal interface across normal pregnancies from early gestation to term by integrating large-scale paired single-nucleus transcriptomic and chromatin accessibil…
Open article record in new tab ↗This paper focuses on school climate indicators, which have been previously linked with aspects of students' well-being and school-related success, to explore how they relate to alcohol and cannabis use. We used machine learning (ML) approaches and leveraged data from a diverse sample of 69,513 students (45.4% White, 23.9% Black, 8.9% Latine) across 111 middle and high schools, with 12% (n = 7783) reporting cannab…
Open article record in new tab ↗BACKGROUND: Hypertension (HTN) results from intricate molecular mechanisms, making clinical remission difficult to achieve. This study explores the molecular pathways through which cannabidiol (CBD) may influence HTN. METHODS: Several RNA sequencing datasets related to HTN were retrieved from the GEO database and divided into training and validation sets. Candidate genes potentially associated with HTN were screen…
Open article record in new tab ↗This study uses keyword filtering, a transformer-based algorithm, and inductive content coding to identify and characterize cannabis adverse experiences as discussed on the social media platform Reddit and reports a total of 1177 self-reported adverse experiences requiring medical attention.
Open article record in new tab ↗This study presents a machine learning framework for the reconstruction of fatigue life and fracture toughness in natural fiber-reinforced composites, evaluating the predictive accuracy of six regression algorithms-Random Forest, Gradient Boosting, Support Vector Machine, Neural Network, Ridge Regression, and Lasso Regression-using a controlled synthetic dataset of 600 samples generated from established Basquin fa…
Open article record in new tab ↗Background Machine learning (ML) has growing potential to support early identification of high-risk pregnancies in resource-constrained settings. However, most studies focus on model development and predictive performance, with less attention to the health-system processes required to generate ML-ready data and translate risk information into clinical action. The Mlinde Mama Project in Tanzania combined Group Ante…
The principal objective served by this article is to identify key literature and provide an overview of the breadth of research in the field of machine learning applications on exposomics data with a focus on cardiovascular diseases. Secondarily, this study aimed at identifying common limitations and meaningful directives to be addressed in the future. Most of the identified literature focuses on Disease Understan…
Open article record in new tab ↗Early identification of patients with advanced chronic conditions (MACA) remains a critical challenge in clinical practice, often relying on retrospective criteria or clinical judgment, which may delay timely and personalized intervention. The increasing availability of electronic health records (EHR) enables the application of Machine Learning (ML) techniques to support more proactive detection. This study aimed…
BackgroundHypertension remains one of the most challenging healthcare problems in the community. It is a common, measurable, and treatable condition that is nonetheless responsible for millions of preventable deaths each year. Hypertension affects approximately 1.28 billion adults worldwide, yet fewer than half (46%) are aware of their condition. Undiagnosed hypertension is a critical public health gap, contributi…
ObjectiveThis study aimed to train and evaluate supervised machine learning walgorithms using electronic health record (EHR) data to accurately estimate gestational age at delivery. Materials and MethodsWe trained random forest, gradient boosting, and ensemble models on EHR data of mother-infant dyads from Vanderbilt University Medical Center(VUMC) and replicated the analyses at University of Michigan (UMich). We…
ObjectivesTo develop and demonstrate a practical post-hoc, model-agnostic fairness auditing tool that integrates group-level and individual-level fairness assessment for clinical prediction models. Materials and MethodsWe developed EquiLense, a fairness auditing tool that operationalizes three components: group fairness evaluation using established and novel group-level disparity metrics, individual fairness asses…
Open article record in new tab ↗BackgroundDepressive symptoms among reproductive-aged women represent a major public health concern in low- and middle-income countries, yet systematic screening remains limited. In most population survey datasets, the low prevalence of depression results in severe class imbalance, which challenges conventional machine learning models. Therefore, we develop and evaluate a bagging-based ensemble machine learning fr…
BackgroundNepal is experiencing a rapid demographic shift toward an aging population, with concurrent increase in morbidity and medication-related problems. Despite this, the multidimensional experience of medication-related burden (MRB) and refill adherence remain under-studied, particularly through the lens of socio-demographic, clinical and medication-related predictive features. This study aimed to assess MRB…
Open article record in new tab ↗Deep learning models utilizing longitudinal healthcare data have significantly advanced epidemiological research. However, contemporary transformer-based models increasingly rely on computationally intensive pre-training steps that entail processing massive real-world datasets with cost-prohibitive hardware. We introduce the Temporal Encoder with Late Fusion (TELF), a lightweight end-to-end predictive model featur…
Open article record in new tab ↗BackgroundChronic kidney disease (CKD) affects approximately 850 million individuals worldwide and remains a leading cause of morbidity, premature mortality, and escalating healthcare costs. Despite the availability of clinical biomarkers, CKD progression to end-stage renal disease (ESRD) is frequently identified late, limiting opportunities for preventive intervention. Conventional predictive models have relied p…
Open article record in new tab ↗BackgroundSnakebite envenoming is a significant global health crisis that has been long neglected as a global health priority. It is a huge problem for rural communities of low and middle-income countries, India accounts for the largest proportion of snakebite deaths globally. Timely identification of venomous snakebite and its syndromic pattern is essential for effective administration of antivenom and supportive…