Prediction of Major Clinical Endpoints in Atrial Fibrillation at Primary Care Level using Longitudinal Learning Stances

Abstract excerpt

Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia worldwide and is strongly associated with increased risks of stroke, heart failure, and mortality. Traditional methods to predict AF and prognostic its associated risks often fail to capture the full complexity of AF patterns, limiting their predictive accuracy. In spite of the improvements achieved by machine learning (ML) techniques, state-of-the-...

Topics

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• Atrial Fibrillation
• Electrocardiography, Ambulatory
• Electronic Health Records
• Longitudinal Studies
• Multicenter Studies as Topic
• Predictive Value of Tests
• Research Design
• Risk Assessment

Identifiers and source

Literature Corpus work

6ae3d569-59fe-592a-ad2c-f329d771160a

DOI

10.64898/2026.03.26.26349389