Prediction of 90 Day Mortality in Elderly Patients with Acute HF from e-Health Records Using Artificial Intelligence
The study proposed a robust model for the automatic prediction of 90 day mortality risk 48 h after hospitalization for decompensated HF. This could be used to identify high-risk patients for intensification of therapeutic management.
Authors
- D. Theis
- Pascal de Groote (ORCID: https://orcid.org/0000-0002-6211-0147)
- A. Buronfosse (ORCID: https://orcid.org/0000-0002-6593-8935)
- Fabio Boudis
- Amélie Bruandet (ORCID: https://orcid.org/0000-0001-7180-8558)
- Joconde Weller (ORCID: https://orcid.org/0000-0002-4367-3470)
- Léo Pellet
- Pierre Zweigenbaum
- Michel Komajda
- Romain Cador
- Marie Joseph Aroulanda
- J. Gutton
- Guillaume Hocquet
Institutions
- Inserm (FR)
- Université Sorbonne Nouvelle (FR)
- Université Paris-Saclay (FR)
- Institut Pasteur de Lille (FR)
- Centre Hospitalier Universitaire de Lille (FR)
- Sorbonne Université (FR)
- Centre d'Économie de la Sorbonne (FR)
- Lille’s Cardiology Hospital (FR)
- Hôpital Saint Joseph (FR)
- Hôpital Paris Saint-Joseph (FR)
- Université Paris 1 Panthéon-Sorbonne (FR)
Publication Details
- Journal
- ESC Heart Failure
- Published
- 2025-02-13
- DOI
- https://doi.org/10.1002/ehf2.15244
- Citations
- 3
- Primary Topic
- Heart Failure Treatment and Management
- Type
- article
- Field-Weighted Citation Impact
- 4.16
Funders
- Agence Nationale de la Recherche