Machine learning-based prediction of sudden cardiac death in the general population using electronic health record data
We propose a prediction model designed to identify individuals at high risk of SCD among the general population. When combined with EHR, this artificial intelligence model has the potential to assist in risk stratification and may help inform the prioritization of preventive strategies, contributing to more targeted use of cardiovascular health resources.
Authors
- Wulfran Bougouin (ORCID: https://orcid.org/0000-0002-1821-7713)
- Thomas Laurenceau (ORCID: https://orcid.org/0000-0002-6927-9893)
- Xavier Jouven (ORCID: https://orcid.org/0000-0002-4439-0093)
- Jean-Philippe Empana
- Richard Chocron
- Frankie Beganton
- Tom Rea
- Younès Youssfi
- Nicolas Chopin
Institutions
- Inserm (FR)
- University of Washington (US)
- Université Paris Cité (FR)
- Hôpital Européen Georges-Pompidou (FR)
- Délégation Paris 6 (FR)
- Paris Cardiovascular Research Center (FR)
- European Hospital (IT)
- National Institute of Statistics and Economic Studies (FR)
- Hôpital Privé Jacques Cartier (FR)
Publication Details
- Journal
- European Journal of Preventive Cardiology
- Published
- 2026-02-05
- DOI
- https://doi.org/10.1093/eurjpc/zwag087
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00