Latest AI and machine learning research in hospital-based medicine for healthcare professionals.
Objective: Stigmatizing language in the electronic health record (EHR) has been associated with adve...
Background: Hospital incident risk scoring has long relied on two- or three-dimensional frameworks (...
Background: Sepsis is a life-threatening condition in which delayed recognition and treatment are as...
Background: Adverse drug events (ADEs) are a critical indicator of patient safety but are often docu...
Introduction: The use of artificial intelligence (AI) by clinicians has increased rapidly in recent ...
Background: Non-communicable diseases (NCDs) represent a critical public health challenge in Kenya, ...
Pathology foundation models (PFMs) have advanced rapidly in recent years and support training classi...
Background: Professionalism and effective communication are foundational determinants of patient saf...
Hospital antimicrobial resistance (AMR) emanates from an array of complex interactions between patie...
Importance: Abdominal pain causes roughly 10 million US emergency department (ED) visits annually, m...
Objective: Readmissions to the PICU are associated with increased morbidity and mortality. A predict...
Background: Large language models (LLMs) demonstrate strong performance in controlled medical enviro...
Introduction: Infectious and wound-healing complications after colorectal surgery often increase the...
Inpatient medication recommendation requires clinicians to repeatedly select specific medications, d...
Intensive Care Unit (ICU) readmissions are associated with adverse clinical outcomes and increased h...
Delirium, a dynamic neuropsychiatric condition associated with morbidity and mortality, remains unde...
In the data-driven era, large-scale datasets are routinely collected and analyzed using machine lear...
Timely identification of hospital inpatients at risk of deterioration facilitates interventions to s...
This study explores integrating machine learning into electronic medical record systems to predict s...
This paper presents a reproducible and process-aware pipeline for predictive monitoring of clinical ...
Objectives This study aimed to develop and validate machine learning models to predict in-hospital m...