Latest AI and machine learning research in hospital-based medicine for healthcare professionals.
Patient healthcare utilization consists of irregularly time-stamped events, such as outpatient visits, inpatient admissions, and emergency encounters, forming individualized care trajectories. Modeling these trajectories is crucial for understanding utilization patterns and predicting future care needs, but is challenging due to temporal irregularity and severe class imbalance. In this work, we bu...
High inpatient opioid exposure is associated with increased risk of persistent opioid use. Early identification of high-risk patients may improve opioid stewardship. We developed machine learning models to predict high opioid exposure during hospitalization using electronic health record data from MIMIC-IV. We conducted a retrospective study of 223,452 unique first hospital admissions in MIMIC-IV....
Cardiac surgery patients experience rapidly evolving hemodynamics in early post-operative period requiring intensive support. Identifying hemodynamic ...
Hypertrophic cardiomyopathy (HCM) requires accurate risk stratification to inform decisions regarding ICD therapy and follow-up management. Current es...
Background: Artificial intelligence (AI) has rapidly garnered interest in healthcare, with research showing promise to improve quality, efficiency, an...
Purpose: Early recognition of deterioration in patients with suspected infection at the emergency department (ED) is important. Current clinical scori...
Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia worldwide and is strongly associated with increased risks of stroke, heart failure, ...
Background Narcolepsy is a rare, lifelong neurological disorder that often begins in childhood or adolescence. Diagnosis is frequently delayed because...
Background Chronic subdural hematoma (cSDH) recurrence requiring reoperation occurs in 5-33% of cases, representing a substantial clinical and economi...
This study assesses whether self-supervised learning (SSL) improves knee osteoarthritis (OA) modeling for diagnosis and prognosis relative to ImageNet...
Deploying clinical ML is slow and brittle: models that work at one hospital often degrade under distribution shifts at the next. In this work, we stud...
Modern clinical practice increasingly depends on reasoning over heterogeneous, evolving, and incomplete patient data. Although recent advances in mult...
Epilepsy is a chronic neurological disorder requiring multi-faceted management, including seizure detection, syndrome diagnosis, prognostication, anti...
Modern clinical practice increasingly depends on reasoning over heterogeneous, evolving, and incomplete patient data. Although recent advances in mult...
Pathology reports are structured, multi-granular documents encoding diagnostic conclusions, histological grades, and ancillary test results across one...
Objective: To develop and validate a multivariable prediction model and clinically actionable risk score for vaginal birth after cesarean (VBAC) succe...
Background: The administrative burden of clinical documentation is a recognised contributor to clinician burnout and diminished care quality. Ambient ...
Objective: To evaluate a ranking approach for emergency department (ED) waiting room prioritization that uses pairwise clinical comparisons aggregated...
Background: This study aims to develop and validate federated learning models for predicting major postoperative complications and mortality using a l...
Brief Hospital Course (BHC) narratives must be clinically useful yet faithful to fragmented EHR evidence. LLM-based clinical summarizers still introdu...