Electronic Health Record-Based Machine-Learning for Cardiac Arrhythmia Identification in Adults with High-Risk Chronic Disease: Model Development and Validation Study

Background: Cardiac arrhythmias may remain unrecognized in adults with chronic disease. Established risk scores primarily address atrial fibrillation. Objective: To develop and internally evaluate an electronic health record (EHR)-based machine-learning model to identify patients more likely to receive a documented cardiac arrhythmia diagnosis. Methods: We retrospectively evaluated EHR data (2018-2025) from one healthcare organization among adults with type 2 diabetes, chronic obstructive pulmonary disease, chronic kidney disease, or coronary artery disease. Logistic regression, random forest,

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