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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.

Disease profiles and access to care differ between rural and urban populations. The aim of this study is to examine the association of rurality with electronic health record-derived disease profiles and heart failure risk.

Childhood obesity and respiratory tract infections (RTIs) are 2 major global public health issues that frequently co-occur and are closely interrelated. Early detection of children with prior RTIs who are at high obesity risk is crucial for targeted interventions. This study integrates interpretable machine learning (ML) models and a deep learning network to develop an obesity risk prediction model in a large pediatric cohort.

Clinicians are increasingly using emoji in digital communication, but limited qualitative work has examined how they assess the appropriateness, risks, and benefits of this practice.

Embedded pragmatic clinical trials (ePCTs) are conducted as part of routine clinical care and therefore use data collected from real-world data sources, such as electronic health record systems and administrative claims. A common approach for using these types of data across all phases of trial conduct is to create a computable phenotype (an explicitly defined data query data, including specified data types, data codes, and logical parameters) to capture patients [...]