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This study aimed to identify trajectories of multimorbidity following acute myocardial infarction (AMI), using explainable temporal machine-learning methods, and assess their clinical, prognostic, and biological significance.

The potential of "big data" in health research remains largely untapped, particularly concerning real-world data sources such as administrative health data and electronic medical records. While healthcare insurance claims data have been essential for assessing medication safety and effectiveness within indicated patient populations, exploring broader drug-outcome associations could uncover significant insights.

To develop and systematically compare a human-led and LLM-assisted hybrid deductive-inductive workflow for qualitative analyses.

Accurate citation of relevant publications is essential for scientific integrity in biomedical research. Large language models (LLMs) excel at text generation but often hallucinate fabricated or inaccurate citations. Retrieval-augmented generation (RAG) can mitigate these errors, yet current approaches lack semantic precision in evidence retrieval. This study aims to develop a domain-specific RAG system for reliable, context-specific biomedical citation recommendations.

Large-scale propensity score (LSPS) models are increasingly used to control confounding in observational studies, but their reliability in small-sample settings is unclear. Small samples can limit a study's ability to estimate propensity scores accurately and achieve adequate covariate balance, raising concerns about insufficient confounding adjustment. Resulting in small datasets being excluded, despite potentially containing valid information.

Patients with substance misuse are at high risk for clinical deterioration, and pre-hospital encounters constitute important risk factors. We sought to incorporate these risk factors into a novel prediction model using linked electronic health record, emergency medical services (EMSs), and claims data.

To evaluate natural language processing (NLP) and machine learning (ML) approaches for identifying social needs in electronic health record (EHR) notes, using balanced versus real-world imbalanced datasets.

To examine how algorithmic fairness is measured, operationalized, and reported in machine learning (ML) models designed to predict or support secondary prevention of cardiovascular disease (CVD) outcomes including progression, recurrence, readmission, and post-index mortality in racialized populations.

Investigate how patients at a Federally-Qualified Health Center (FQHC) perform with, and perceive, complex telehealth tasks. Determine which complexity dimensions most affect patients' performance and perceptions, and compare this to researcher-evaluators' complexity walkthrough results.

Substance use disorder (SUD) care continues to be hindered by persistent gaps in health information exchange (HIE). This study examined people, organizational, regulatory, and technical barriers to SUD data sharing from the provider perspective and identified key opportunities to improve care through health record interoperability.