Latest AI and machine learning research in hospital-based medicine for healthcare professionals.
Involving end-users in the development of an AI tool is an important facilitator to its implementation. Usability testing was therefore conducted with a prototype user interface of an Artificial Intelligence driven air-Safety Tool (AISaT) to capture the perspectives and user experiences of AISaT from 10 staff members across two hospitals working within estates, infection prevention and control, an...
Traditional comorbidity scores (e.g., Charlson and Elixhauser) are widely used for risk adjustment and patient stratification, but they have two key limitations: (i) they are largely mortality-centric and do not align well with other clinical outcomes, and (ii) their linear, rule-based structure cannot capture nonlinear, outcome-specific risk relationships. We propose a Machine-Learned Comorbidity...
In primary care and outpatient settings, clinically important patient information is often embedded in fragmented, ambiguous, repetitive, and noisy co...
Personalized medicine in acute ischemic stroke requires moving beyond average treatment effects (ATE) to individualized treatment effect (ITE) estimat...
Objective. To introduce PsiBench, a clinically validated medication-safety benchmark for evaluating large language models (LLMs) against the standards...
Background: Unstructured data represent about 80% of total electronic health records (EHR) data. Structuring this free text is essential for advancing...
Post-surgical adverse outcomes, including mortality, intensive care readmission, and complications, remain major challenges for clinical decision-maki...
Background: Digital decision-support tools such as triage systems and symptom checkers support millions of health-related decisions each year. Their q...
Background. Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia and a major determinant of prognosis. Established AF risk scores rely on...
Background. Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia and a major determinant of prognosis. Established AF risk scores rely on...
Background: Aortic stenosis (AS) is a progressive valvular disease associated with poor prognosis once symptoms develop, yet routine echocardiographic...
End-of-rotation handoffs are critical for patient safety but add to documentation burden for hospitalists. Generative artificial intelligence (AI) may...
Since the U.S. 2013/14 influenza season, the CDC's FluSight Challenge has provided a platform for evaluating influenza forecasting models and fosterin...
Background Machine learning (ML) models are increasingly used to predict adverse outcomes after surgery. However, most rely on static patient characte...
Background: Safe reuse of multimodal hospital data for AI development is limited by the absence of reliable, context-aware deidentification across mul...
Although large language models (LLMs) have shown promise for discharge summary generation, their value may be greater in longer hospitalizations, wher...
Identifying robust biomarkers from high-dimensional biomedical data is a central challenge in translational research, but candidate rankings produced ...
Large language models are increasingly used for structured extraction from clinical free-text notes, but the sensitivity of their output to upstream c...
Background: We previously examined the burden and predictors of sustained mental health care engagement in Ugandan first episode psychosis patients by...
Objective: To develop, calibrate, and interpret machine learning models for predicting in-hospital mortality among intensive care unit (ICU) patients ...