Machine learning to risk stratify individuals for undiagnosed atrial fibrillation at scale using population-wide electronic health records
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.
Authors
- Lior Rokach (ORCID: https://orcid.org/0000-0002-6956-3341)
- Keerthenan Raveendra
- Talish Razi Benita
- D Zahger
- Mohammad Haris
- J Wu
- Moti Haim
- Chris P Gale
- Ronen Arbel
Institutions
- Ben-Gurion University of the Negev (IL)
- University of Leeds (GB)
- Sapir College (IL)
- Queen Mary University of London (GB)
- Leeds Teaching Hospitals NHS Trust (GB)
- Soroka Medical Center (IL)
- Leeds Dental Hospital (GB)
Publication Details
- Journal
- European Journal of Preventive Cardiology
- Published
- 2026-02-23
- DOI
- https://doi.org/10.1093/eurjpc/zwag124
- Primary Topic
- Atrial Fibrillation Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.00