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Electronic health record (EHR) time series are irregularly sampled and frequently incomplete, with missingness potentially carrying clinically informative signals. We developed Masked-Attention-based timestamp-wise Data Embedding (MADE), a missingness-aware model for continuous prediction of deterioration in general wards, and evaluated it across heterogeneous hospitals.

To systematically synthesize how data quality (DQ) in disease and health outcomes registries has been conceptualized, assessed, and improved, emphasizing artificial intelligence (AI) and machine learning methods within registry-based DQ workflows.

While there are numerous mobile health (mHealth) apps on the market, previous research has highlighted their risks to privacy and security. Therefore, an instrument to assess the privacy and security of these apps may be helpful for ensuring consumers' data safety. We therefore conducted this rapid scoping review with the objective of surveying the landscape for instruments used to measure and quantify privacy and/or security of mHealth apps in the [...]

To examine posthumous health data donation as a pathway toward more representative datasets used to develop healthcare artificial intelligence (AI).

To externally validate a pediatric surgical site infection (SSI) prediction model, assess institutional and temporal transportability, and evaluate recalibration strategies to support adaptation of registry-derived predictions for clinical decision support (CDS).

To develop an electronic health record (EHR)-based multimorbidity-weighted index (eMWI) using computable phenotypes mapped to the Observational Medical Outcomes Partnership (OMOP) common data model and previously validated physical functioning-based condition weights.

Older adults face disproportionate risk of diagnostic errors in primary care, yet effective diagnostic decision support systems (DDSSs) remain lacking. Existing artificial intelligence (AI)-based DDSSs rely on gold-standard labels infeasible to obtain, have narrow diagnostic scope, and fail to reflect real-world uncertainty. We developed and validated a deep learning DDSS using imitation learning and collective clinician intelligence to generate diagnostic and order recommendations.

Pregnant and breastfeeding women, along with children, represent populations persistently underrepresented in clinical research, leading to uncertainty in clinical decision-making. This study investigated the landscape of pharmacology research concerning pharmacokinetics (PK), pharmaco-epidemiology (PE), and clinical trials (CT) within maternal and pediatric patient populations.

To develop and validate an open-source evaluation framework that assesses clinical AI diagnostic systems across multiple clinically relevant dimensions (accuracy, cost, time, invasiveness, physician effort, and safety behaviors), addressing the limitations of accuracy-only benchmarks.