Machine Learning Methods for Predicting Syncope Severity in the Emergency Department: A Retrospective Analysis
This study provides an experimental foundation for the application of ML techniques in managing syncope in ED. The intention is to stimulate AI research in this area, with a view to integrating these models into clinical workflows in the future.
Authors
- Benjamín Sahelices (ORCID: https://orcid.org/0000-0002-3380-3403)
- Isabel de la Torre Díez (ORCID: https://orcid.org/0000-0003-3134-7720)
- Jesús Vegas (ORCID: https://orcid.org/0000-0001-6568-4958)
- Rosmeri Martínez‐Licort (ORCID: https://orcid.org/0000-0001-6916-4773)
Institutions
- Universidad de Valladolid (ES)
Publication Details
- Journal
- Health Science Reports
- Published
- 2025-02-01
- DOI
- https://doi.org/10.1002/hsr2.70477
- Citations
- 1
- Primary Topic
- Cardiovascular Syncope and Autonomic Disorders
- Type
- article
- Field-Weighted Citation Impact
- 2.01
Funders
- Universidad de Valladolid