Machine learning to risk stratify individuals for undiagnosed atrial fibrillation at scale using population-wide electronic health records

20260 citationsJournal Articlehybrid Open Access

Authors

Lior Rokach · Ben-Gurion University of the Negev

Talish Razi Benita · Ben-Gurion University of the Negev

Ramesh Nadarajah · University of Leeds

Keerthenan Raveendra · University of Leeds

Mohammad Haris · University of Leeds

Chris P Gale · University of Leeds

J Wu · Queen Mary University of London

Abstract

The FIND-AF machine learning algorithm was scalable in routine EHR data with good discrimination for incident AF. Prospective evaluation is now required to evaluate risk-guided AF screening.

Topics & Keywords

Atrial Fibrillation Management and Outcomes ECG Monitoring and Analysis Imbalanced Data Classification Techniques

UN Sustainable Development Goals

Reduced inequalities

Publication Details

Published in: European Journal of Preventive Cardiology

DOI: 10.1093/eurjpc/zwag124

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