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Machine-learning-based clinical risk prediction models are increasingly used to support decision-making in healthcare. While class-imbalance correction techniques are commonly applied to address rare outcomes, their impact on probabilistic calibration remains insufficiently understood. This study evaluated the effect of widely used resampling strategies on both discrimination and calibration across real-world clinical prediction tasks.

Data modernization (DM) seeks to transform data and information systems in public health (PH) to support core activities. This study characterizes incumbent PH workers who perform informatics and data-centric roles that are critical to DM activities.

To evaluate whether real-time electronic health record (EHR)-based secure messaging between emergency department (ED) clinicians and prior discharge teams influences ED disposition decisions for patients re-presenting within 30 days of hospital discharge.

To determine whether a greater volume of health information exchange (HIE) between care delivery systems improves patient outcomes and whether effects differ between community care and Veterans Health Administration (VHA) direct care settings.

Long COVID (LC) remains poorly understood, and there is a critical need for advanced computational tools to better identify and characterize patients. In this study, we use summarized symptom reports by RECOVER-Adult cohort participants linked to EHR data to characterize patients and train a computable phenotype algorithm of LC.

The analysis of care trajectories derived from electronic health records and claims data has become increasingly common in biomedical informatics. This has enabled large-scale studies of care processes, yet widely used binary code representations result in high-dimensional, sparse data that fail to capture semantic relationships between medical concepts. Learning dense vector representations (embeddings) has emerged as a promising approach to address these limitations. We aimed to construct and share joint [...]

The successful integration of Machine Learning (ML) models into clinical practice remains limited, as they often lack the standardized, quantifiable risk measures essential for clinical workflows. This study, therefore, aims to demonstrate the Unified Auto Clinical Scores (Uni-ACS) method as a means to translate ML predictions into interpretable biostatistical formats, a translation critical for clinical adoption and alignment with evidence-based practice guidelines.

To describe considerations for integration of human and artificial intelligence for creating a postmarketing surveillance system capable of timely and reliably identifying causal effects of medications on safety endpoints.

Distributed Research Networks (DRNs) offer significant opportunities for collaborative multi-site research and have significantly advanced healthcare research based on clinical observational data. However, generating high-quality real-world evidence using fit-for-use data from multi-site studies faces important challenges, including biases associated with various types of heterogeneity within and across sites and data sharing difficulties. Over the last 10 years, Privacy-Preserving Distributed Algorithms (PDA) have been developed and utilized in numerous national and [...]