Latest AI and machine learning research in information technology for healthcare professionals.
Real-world systems must continuously adapt to novel concepts from limited data without forgetting previously acquired knowledge. While Few-Shot Class-Incremental Learning (FSCIL) is established in computer vision, its application to tabular domains remains largely unexplored. Unlike images, tabular streams (e.g., logs, sensors) offer abundant unlabeled data, a scarcity of expert annotations and ne...
Background: Cardiovascular disease remains the leading cause of global morbidity and mortality. The original My Heart Counts smartphone application demonstrated the feasibility of large-scale, fully digital recruitment and trial conduct, but was limited by platform exclusivity and the need for human experts to create text-based behavioral interventions. Methods: The next-generation My Heart Counts...
Background: Artificial intelligence is increasingly embedded in healthcare delivery. Its legitimacy depends on institutional governance, not technical...
Reliable Alzheimer's disease (AD) diagnosis increasingly relies on multimodal assessments combining structural Magnetic Resonance Imaging (MRI) and El...
Background Generative artificial intelligence (GenAI) in healthcare may reduce administrative burden and enhance quality of care. Large language model...
Introduction: Recreational and medical cannabis use (CU) information is often available within the electronic health record (EHR) in a format that is ...
Rationale, Aims and Objectives: Unwarranted clinical variation (UCV) in patient care often arises from contextual factors and contributes to increased...
Machine learning holds promise for advancing clinical decision support, yet it remains unclear when multimodal learning truly helps in practice, parti...
Longitudinal electronic health record (EHR) data are often left-censored, making diagnosis records incomplete and unreliable for determining disease o...
Pediatric asthma exacerbations are a frequent cause of emergency department (ED) visits and hospitalizations, yet accurate risk prediction remains lim...
Background: Typing in the electronic health record (EHR) takes up healthcare providers' time and cognitive space and constitutes a substantial adminis...
Machine learning holds great promise for advancing the field of medicine, with electronic health records (EHRs) serving as a primary data source. Howe...
Temporal information in structured electronic health records (EHRs) is often lost in sparse one-hot or count-based representations, while sequence mod...
Background Epilepsy is a common neurologic disorder characterized by recurrent, unprovoked seizures. Epilepsy manifests as different seizure types and...
Background: EHR documentation and chart review contribute to clinician workload and burnout. To alleviate pre-charting burden, Epic has released a new...
Electronic health records (EHRs) have become the cornerstone of population-scale genetic studies1, but factors including patterns of healthcare use sh...
Objective: Electronic Health Record (EHR)-based trial emulation can support translation of randomized clinical trial (RCT) evidence into practice, yet...
Latent space models are widely used for analyzing high-dimensional discrete data matrices, such as patient-feature matrices in electronic health recor...
Learning from electronic health records (EHRs) time series is challenging due to irregular sam- pling, heterogeneous missingness, and the resulting sp...
Background: Large language models (LLMs) are increasingly piloted as chat interfaces for chart review and clinical decision support. Although leading ...