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Chest pain is common, and current risk-stratification methods, requiring 12-lead electrocardiograms (ECGs) and serial biomarker assays, are static and restricted to highly resourced settings. Our objective was to predict myocardial injury using continuous single-lead ECG waveforms similar to those obtained from wearable devices and to evaluate the potential of transfer learning from labeled 12-lead ECGs to improve these predictions.

The Anesthesiology Control Tower (ACT) for operating rooms (ORs) remotely assesses the progress of surgeries and provides real-time perioperative risk alerts, communicating risk mitigation recommendations to bedside clinicians. We aim to identify and map ACT-OR nonroutine events (NREs)-risk-inducing or risk-mitigating workflow deviations-and ascertain ACT's impact on clinical workflow and patient safety.

Synthetic data are increasingly relied upon to share electronic health record (EHR) data while maintaining patient privacy. Current simulation methods can generate longitudinal data, but the results are unreliable for several reasons. First, the synthetic data drifts from the real data distribution over time. Second, the typical approach to quality assessment, which is based on the extent to which real records can be distinguished from synthetic records using a critic [...]

Despite efforts to improve screening and early detection of prostate cancer (PC), no available biomarker has shown acceptable performance in patients with prostate-specific antigen (PSA) gray zones. We aimed to develop a deep learning-based prediction model with minimized parameters and missing value handling algorithms for PC and clinically significant PC (CSPC).

To develop a usability checklist for public health dashboards.

To determine the extent of implementation, completeness, and accuracy of Structured and Codified SIG (S&C SIG) directions on electronic prescriptions (e-prescriptions).

Abnormalities in impulse propagation and cardiac repolarization are frequent in hypertrophic cardiomyopathy (HCM), leading to abnormalities in 12-lead electrocardiograms (ECGs). Computational ECG analysis can identify electrophysiological and structural remodeling and predict arrhythmias. This requires accurate ECG segmentation. It is unknown whether current segmentation methods developed using datasets containing annotations for mostly normal heartbeats perform well in HCM. Here, we present a segmentation method to effectively identify ECG waves across 12-lead [...]

Prediction models can be useful tools for monitoring patient status and personalizing treatment in health care. The goal of this study was to compare the relative strengths and weaknesses of 2 different approaches for predicting functional recovery after knee arthroplasty: a neighbors-based "people-like-me" (PLM) approach and a linear mixed model (LMM) approach.

Plain language in medicine has long been advocated as a way to improve patient understanding and engagement. As the field of Natural Language Processing has progressed, increasingly sophisticated methods have been explored for the automatic simplification of existing biomedical text for consumers. We survey the literature in this area with the goals of characterizing approaches and applications, summarizing existing resources, and identifying remaining challenges.

Visual timelines of patient-reported outcomes (PRO) can help prostate cancer survivors manage longitudinal data, compare with population averages, and consider future trajectories. PRO visualizations are most effective when designed with deliberate consideration of users. Yet, graph literacy is often overlooked as a design constraint, particularly when users with limited graph literacy are not engaged in their development. We conducted user testing to assess comprehension, utility, and preference of longitudinal PRO [...]