Latest AI and machine learning research in hospital-based medicine for healthcare professionals.
With the proliferation of Electronic Health Records (EHRs), a critical challenge in building predict...
Introduction: Polypharmacy in older adults is associated with increased risks of adverse drug events...
Measurement-critical ultrasound tasks often depend on a small anatomical region, making global recon...
Importance: Physicians routinely prognosticate to guide care delivery and shared decision making, pa...
Background Outcome after stroke varies according to stroke subtype by location, but healthcare syste...
Clinical risk prediction using longitudinal medical data supports individualized care. Self-supervis...
Objective: To propose and retrospectively validate an integrated framework addressing three barriers...
Temporal resolution of physiological monitoring in intensive care varies widely across healthcare sy...
Modern medicine generates vast multimodal data across siloed systems, yet no existing model integrat...
Inadequate discharge communication is a well-documented contributor to medication non-adherence, mis...
Background: The urgent care departments in Europe face a structural paradox: accelerating digitalisa...
Study ObjectivesTo evaluate wearable sleep staging across sleep apnea severity, including very sever...
Patient healthcare utilization consists of irregularly time-stamped events, such as outpatient visit...
High inpatient opioid exposure is associated with increased risk of persistent opioid use. Early ide...
Cardiac surgery patients experience rapidly evolving hemodynamics in early post-operative period req...
Hypertrophic cardiomyopathy (HCM) requires accurate risk stratification to inform decisions regardin...
Background: Artificial intelligence (AI) has rapidly garnered interest in healthcare, with research ...
Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia worldwide and is strongly associat...
Background Narcolepsy is a rare, lifelong neurological disorder that often begins in childhood or ad...
Deploying clinical ML is slow and brittle: models that work at one hospital often degrade under dist...
Modern clinical practice increasingly depends on reasoning over heterogeneous, evolving, and incompl...