Dynamic prediction of paroxysmal atrial fibrillation onset using longitudinal sample entropy in joint models
These results demonstrate the potential of joint models for short-term AF risk prediction, providing not only binary classification but also dynamic, individualized risk estimates over time. They enable updated predictions and personalized monitoring of patient risk as new longitudinal data become available.
Authors
- Nicolas Ngo
- Aline Campos Reis de Souza (ORCID: https://orcid.org/0000-0002-5196-0672)
- Roch Giorgi
Institutions
- Universidade Estadual de Maringá (BR)
- Inserm (FR)
- Aix-Marseille Université (FR)
- Hôpital Sainte-Marguerite (FR)
- Economic & Social Sciences, Health Systems & Medical Informatics (FR)
- Institut de Recherche pour le Développement (FR)
Publication Details
- Journal
- BMC Medical Research Methodology
- Published
- 2026-02-09
- DOI
- https://doi.org/10.1186/s12874-026-02773-9
- Primary Topic
- Atrial Fibrillation Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.00