Antihypertensive Drug Recommendations for Reducing Arterial Stiffness in Patients With Hypertension: Machine Learning–Based Multicohort (RIGIPREV) Study
This study demonstrates the utility of ML techniques in providing personalized treatment recommendations for antihypertensive therapy. By accounting for individual patient characteristics, the model improves the selection of drugs that control blood pressure and reduce AS. These findings could significantly aid clinicians in optimizing hypertension management and reducing cardiovascular risk. However, further studies with larger and more diverse populations are necessary to validate these results and extend the model's applicability.
Authors
- Manuel Ángel Gómez-Marcos (ORCID: https://orcid.org/0000-0003-0133-6123)
- Iván Cavero‐Redondo (ORCID: https://orcid.org/0000-0003-2617-0430)
- Alicia Saz‐Lara (ORCID: https://orcid.org/0000-0003-0669-8625)
- Arturo Martínez‐Rodrigo (ORCID: https://orcid.org/0000-0003-2343-3186)
- Luis García‐Ortiz (ORCID: https://orcid.org/0000-0001-6555-8302)
- Leticia Gómez‐Sánchez (ORCID: https://orcid.org/0000-0001-8663-9339)
- Nerea Moreno-Herráiz (ORCID: https://orcid.org/0009-0007-5385-1406)
- Verónica Casado Vicente (ORCID: https://orcid.org/0000-0002-0576-2827)
Institutions
- Universidad de Valladolid (ES)
- Universidad de Salamanca (ES)
- Hospital Universitario La Paz (ES)
- Universidad Autónoma de Chile (CL)
- Red de Investigación en Actividades Preventivas y Promoción de la Salud (ES)
- Junta de Castilla y León (ES)
- Instituto de Investigación Biomédica de Salamanca (ES)
- University of Castilla-La Mancha (ES)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2024-10-09
- DOI
- https://doi.org/10.2196/54357
- Citations
- 9
- Primary Topic
- Cardiovascular Health and Disease Prevention
- Type
- article
- Field-Weighted Citation Impact
- 3.62