Machine learning approaches for predicting heart failure readmissions
ML models, particularly those using ensemble methods, significantly outperform traditional models in predicting short-term readmission for patients with HF. These findings highlight the potential of ML to improve clinical decision-making and resource allocation in HF management.
Authors
- María José Legarreta (ORCID: https://orcid.org/0000-0002-1778-5196)
- Aitor Almeida (ORCID: https://orcid.org/0000-0002-1585-4717)
- Susana García‐Gutiérrez (ORCID: https://orcid.org/0000-0002-8474-2479)
- Raúl Quirós López (ORCID: https://orcid.org/0000-0002-2802-2477)
- Unai Zulaika (ORCID: https://orcid.org/0000-0002-7366-9579)
- Nere Larrea (ORCID: https://orcid.org/0000-0001-6168-8214)
- Amaia Pikatza-Huerga (ORCID: https://orcid.org/0009-0003-9080-6242)
- Rodrigo García (ORCID: https://orcid.org/0000-0002-5935-8862)
Institutions
- Universidad de Deusto (ES)
- Hospital Costa del Sol (ES)
- Osasun Sistemen Ikerketa Institutua (ES)
- Hospital de Galdakao (ES)
Publication Details
- Journal
- Postgraduate Medical Journal
- Published
- 2025-06-12
- DOI
- https://doi.org/10.1093/postmj/qgaf102
- Primary Topic
- Heart Failure Treatment and Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Eusko Jaurlaritza
- Instituto de Salud Carlos III
- European Regional Development Fund