Latest AI and machine learning research in information technology for healthcare professionals.
Duplicate records pose significant challenges in customer relationship management (CRM)and healthcar...
Background Personalized medicine promises to tailor treatments to the individual, but it carries a h...
Real-world systems must continuously adapt to novel concepts from limited data without forgetting pr...
Background: Cardiovascular disease remains the leading cause of global morbidity and mortality. The ...
Background: Artificial intelligence is increasingly embedded in healthcare delivery. Its legitimacy ...
Reliable Alzheimer's disease (AD) diagnosis increasingly relies on multimodal assessments combining ...
Background Generative artificial intelligence (GenAI) in healthcare may reduce administrative burden...
Introduction: Recreational and medical cannabis use (CU) information is often available within the e...
Rationale, Aims and Objectives: Unwarranted clinical variation (UCV) in patient care often arises fr...
Machine learning holds promise for advancing clinical decision support, yet it remains unclear when ...
Longitudinal electronic health record (EHR) data are often left-censored, making diagnosis records i...
Pediatric asthma exacerbations are a frequent cause of emergency department (ED) visits and hospital...
Background: Typing in the electronic health record (EHR) takes up healthcare providers' time and cog...
Machine learning holds great promise for advancing the field of medicine, with electronic health rec...
Temporal information in structured electronic health records (EHRs) is often lost in sparse one-hot ...
Background Epilepsy is a common neurologic disorder characterized by recurrent, unprovoked seizures....
Background: EHR documentation and chart review contribute to clinician workload and burnout. To alle...
Electronic health records (EHRs) have become the cornerstone of population-scale genetic studies1, b...
Objective: Electronic Health Record (EHR)-based trial emulation can support translation of randomize...
Latent space models are widely used for analyzing high-dimensional discrete data matrices, such as p...
Learning from electronic health records (EHRs) time series is challenging due to irregular sam- plin...