Readable research linked to original sources
Articles
Browse normalized, publication-ready research with direct links to its evidence and source.
Loading articles data…
Readable research linked to original sources
Browse normalized, publication-ready research with direct links to its evidence and source.
Loading articles data…
Readable research linked to original sources
Browse normalized, publication-ready research with direct links to its evidence and source.
32 articles
Newest firstBACKGROUND: Determining health information quality on online drug platforms is crucial for revealing and shaping substance use practices. The study aims to assess the scope and quality of health-related information on the largest Polish drug forum, Hyperreal. Ultimately, the goal was to explore how the case of Hyperreal illustrates user-driven knowledge construction around drug use and its potential implications f…
Open article record in new tab ↗Using publicly accessible Reddit posts, we developed a manually annotated dataset for traditional and aspect-based sentiment analysis (ABSA) of cannabis-related discussions in the context of pain management. The dataset consists of 479 post-aspect pairs extracted from specific Reddit communities associated with autoimmune rheumatic diseases (ARDs). We filtered posts using a structured list of cannabis-related term…
Open article record in new tab ↗ObjectiveThe priorities of people with mental health challenges should be reflected in the research conducted on their behalf. Quantifying alignment of priorities with the unmet needs of people with lived experience is challenging, and to our knowledge, such alignment has not been extensively studied in bipolar disorder (BD). Natural language processing approaches comparing common topics derived from public forums…
Drug shortages represent persistent supply disruptions in the U.S. pharmaceutical market, threatening patient access and increasing drug costs. Prior research commonly treats shortages as binary events and relies on static designs, limiting insight into how shortage characteristics drive cost escalation. This study uncovers the heterogeneity behind drug shortages and pharmacy acquisition costs of generic non-injec…
Open article record in new tab ↗ObjectivesNatural language processing (NLP) can enable scalable extraction of clinically relevant information from unstructured radiology reports retrieved from electronic healthcare data warehouses, but reliance on externally hosted models may pose cost, privacy, and deployment challenges. We compared self-hosted discriminative and generative NLP pipelines for automated extraction of Prostate Imaging and Reportin…
IntroductionPolypharmacy in older adults is associated with increased risks of adverse drug events and functional decline. Discharge summaries often contain deprescribing recommendations, but these are frequently overlooked due to documentation complexity. ObjectiveTo develop and validate a two-stage hybrid system combining rule-based natural language processing (NLP) and large language model (LLM) for automated e…
Open article record in new tab ↗Background The media has immense power in shaping public narratives surrounding sensitive topics such as substance use. Its portrayals can unintentionally fuel harmful stereotypes and stigma, negatively impacting individuals struggling with addiction, influencing policy decisions, and hindering broader public health efforts. Objective This study aimed to examine how the regional newspaper, The Philadelphia Inquire…
Psychoactive substances used for recreational purposes have mind-altering effects, but systematic evaluation of these effects is largely limited to self-reports. Automated analysis of expressed language (speech and written text) using natural language processing (NLP) tools can provide objective readouts of mental states. In this pre-registered systematic review, we investigate findings from applying the emerging…
Open article record in new tab ↗Background Cannabis use is elevated in youth with depression and attention-deficit/hyperactivity disorder (ADHD), but drivers of this increase remain underexplored. The self-medication hypothesis suggests cannabis is used by patients for mood regulation, a common difficulty in ADHD and depression. This study aimed to examine associations between mood instability and cannabis use in a large, representative clinical…
Modelling the prodrome to severe mental disorders (SMD), including unipolar mood disorders (UMD), bipolar mood disorders (BMD) and psychotic disorders (PSY), should consider both the evolution and interactions of symptoms and substance use (prodromal features) over time. Temporal network analysis can detect causal dependence between and within prodromal features by representing prodromal features as nodes, with th…
BackgroundPatient satisfaction feedback is crucial for hospital service quality, but manual reviews are not possible due to their time-consumption, and traditional natural language processing methods remain inadequate. Large Language Models (LLMs) show promise but are prone to logical hallucinations--fabricated or illogical outputs that limit their reliability (inconsistent performance across repeated uses) and va…
ObjectivesThis study aimed to implement an artificial intelligence-assisted psychiatric triage program, assessing its impact on efficiency and resource optimization. MethodsThis quality improvement initiative recruited patients on the waitlist for psychiatric evaluation at an outpatient hospital. Participants (n=101) completed a digital triage module that used natural language processing and machine learning to re…
Head injuries are a leading global cause of mortality and disability, highlighting the critical need for advanced prognostic tools to inform clinical decision-making and optimize healthcare resource utilization. For the first time, this study introduces a cutting-edge artificial intelligence (AI) framework designed to predict mortality outcomes from head injury narratives. Leveraging deep learning-based natural la…
This study explores the potential of using large language models to assist content analysis by conducting a case study to identify adverse events (AEs) in social media posts. The case study compares ChatGPT's performance with human annotators' in detecting AEs associated with delta-8-tetrahydrocannabinol, a cannabis-derived product. Using the identical instructions given to human annotators, ChatGPT closely approx…
Open article record in new tab ↗Objective To explore how artificial intelligence (AI) methodologies, particularly through the analysis of social media content, can enhance "precision in prevention and health surveillance" (2024 Yearbook topic). The focus is on leveraging advanced data analytics to improve the timeliness and accuracy of identifying emerging health concerns, thus enabling more proactive and effective health interventions. Methods…
Background Substance misuse presents significant global public health challenges. Understanding transitions between substance types and the timing of shifts to polysubstance use is vital to developing effective prevention and recovery strategies. The gateway hypothesis suggests that high-risk substance use is preceded by lower-risk substance use. However, the source of this correlation is hotly contested. While so…
Open article record in new tab ↗Health literacy is essential for individuals to navigate the healthcare system and make informed decisions about their health. Low health literacy levels have been associated with negative health outcomes, particularly among older populations and those financially restricted or with lower educational attainment. Plain language summaries (PLS) are an effective tool to bridge the gap in health literacy by simplifyin…
Open article record in new tab ↗BackgroundLarge language models (LLMs) have shown promising performance in various healthcare domains, but their effectiveness in identifying specific clinical conditions in real medical records is less explored. This study evaluates LLMs for detecting signs of cognitive decline in real electronic health record (EHR) clinical notes, comparing their error profiles with traditional models. The insights gained will i…
BackgroundThe launch of the Chat Generative Pre-trained Transformer (ChatGPT) in November 2022 has attracted public attention and academic interest to large language models (LLMs), facilitating the emergence of many other innovative LLMs. These LLMs have been applied in various fields, including healthcare. Numerous studies have since been conducted regarding how to employ state-of-the-art LLMs in health-related s…
Adverse drug events (ADEs) are the fourth leading cause of death in the US and cost billions of dollars annually in increased healthcare costs. However, few machine-readable databases of ADEs exist, limiting the opportunity to study drug safety on a broader, systematic scale. Recent advances in Natural Language Processing methods, such as BERT models, present an opportunity to accurately extract relevant informati…
Open article record in new tab ↗