Objective Accurate and rapid phenoty** is a prerequisite to leveraging electronic health
records for biomedical research. While early phenoty** relied on rule-based algorithms …

The study aims to develop AICare, an interpretable mortality prediction model, using
electronic medical records (EMR) from follow-up visits for end-stage renal disease (ESRD) …

Recent advancements in sequential modeling applied to Electronic Health Records (EHR)
have greatly influenced prescription recommender systems. While the recent literature on …

With the widespread adoption of electronic health records (EHR) data, deep learning
techniques have been broadly utilized for various health prediction tasks. Nevertheless, the …

The use of Large Language Models (LLMs) in medicine is growing, but their ability to handle
both structured Electronic Health Record (EHR) data and unstructured clinical notes is not …

The inherent complexity of structured longitudinal Electronic Health Records (EHR) data
poses a significant challenge when integrated with Large Language Models (LLMs), which …

Despite the explosion of interest in healthcare AI research, the reproducibility and
benchmarking of those research works are often limited due to the lack of standard …

Population-level disease prediction estimates the number of potential patients of particular
diseases in some location at a future time based on (frequently updated) historical disease …

Learning electronic health records (EHRs) has received emerging attention because of its
capability to facilitate accurate medical diagnosis. Since the EHRs contain enriched …

Abstract Electronic Health Record (EHR) data are captured over time as patients receive
care. Accordingly, variations among patients, such as when a patient presents for care …