Latest AI and machine learning research in hospital-based medicine for healthcare professionals.
Background: Guiding risk-appropriate inpatient thromboprophylaxis requires venous thromboembolism (V...
Federated learning lets institutions train shared models without moving their data, which makes it a...
Evidence-based medicine demands clinical answers that are not only fluent and medically plausible, b...
In primary care and outpatient settings, clinically important patient information is often embedded ...
Personalized medicine in acute ischemic stroke requires moving beyond average treatment effects (ATE...
Objective. To introduce PsiBench, a clinically validated medication-safety benchmark for evaluating ...
Background: Unstructured data represent about 80% of total electronic health records (EHR) data. Str...
Post-surgical adverse outcomes, including mortality, intensive care readmission, and complications, ...
Background: Digital decision-support tools such as triage systems and symptom checkers support milli...
Background. Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia and a major determinan...
Background. Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia and a major determinan...
Background: Aortic stenosis (AS) is a progressive valvular disease associated with poor prognosis on...
End-of-rotation handoffs are critical for patient safety but add to documentation burden for hospita...
Since the U.S. 2013/14 influenza season, the CDC's FluSight Challenge has provided a platform for ev...
Background Machine learning (ML) models are increasingly used to predict adverse outcomes after surg...
Background: Safe reuse of multimodal hospital data for AI development is limited by the absence of r...
Although large language models (LLMs) have shown promise for discharge summary generation, their val...
Identifying robust biomarkers from high-dimensional biomedical data is a central challenge in transl...
Large language models are increasingly used for structured extraction from clinical free-text notes,...
Background: We previously examined the burden and predictors of sustained mental health care engagem...
Objective: To develop, calibrate, and interpret machine learning models for predicting in-hospital m...