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Firearm violence constitutes a public health crisis in the United States, but comprehensive data infrastructure is lacking to study this problem. To address this challenge, we used natural language processing (NLP) to classify court record documents from alleged violent crimes as firearm-related or non-firearm-related.

 There is growing recognition of the need to incorporate informatics education in U.S. residencies. Medical residency training programs are critical in shaping system change and can play a pivotal role by incorporating clinical informatics (CI) based learning into their training requirements.

 Treatment of depressive symptoms in older adults is a growing public health concern. Collecting patient-reported outcomes (PROs) may facilitate efficiently scaling psychotherapy for older adults but user-specific tailoring is needed to improve completion.

 This study explores the results of a rapidly implemented no-cost gratitude intervention designed to address student distress during the coronavirus disease 2019 (COVID-19) pandemic. This intervention focused on shared gratitude journaling with a postimplementation survey of well-being using elements of Seligman's PERMA (Positive emotion, Engagement, Relationships, Meaning, and Accomplishment) model of well-being.

 Evidence supports using patient-generated blood pressure data for better outcomes in hypertension management. However, obstacles like dealing with home-generated paper data sets and questions of validity slowed the meaningful incorporation of home blood pressure into clinical care. As clinicians value patient data more, reliance on digital health solutions for data collection and shared decision-making grows.

Development of search queries for systematic reviews (SRs) is time-consuming. In this work, we capitalize on recent advances in large language models (LLMs) and a relatively large dataset of natural language descriptions of reviews and corresponding Boolean searches to generate Boolean search queries from SR titles and key questions.

Serum creatinine (SCr) is the primary biomarker for assessing kidney function; however, it may lag behind true kidney function, especially in instances of acute kidney injury (AKI). The objective of the work is to develop Nephrocast, a deep-learning model to predict next-day SCr in adult patients treated in the intensive care unit (ICU).

This article focuses on the role of the electronic health record (EHR) to generate meaningful formative feedback for medical students in the clinical setting. Despite the scores of clinical data housed within the EHR, medical educators have only just begun to tap into this data to enhance student learning. Literature to-date has focused almost exclusively on resident education.

We aimed to develop and validate a novel multimodal framework Hierarchical Multi-task Auxiliary Learning (HiMAL) framework, for predicting cognitive composite functions as auxiliary tasks that estimate the longitudinal risk of transition from Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD).