Use of artificial intelligence to assess genetic predisposition to develop critical COVID-19 disease: a comparative study of machine learning models
Machine learning models may help predict progression into critical COVID-19-disease. The predictive power of models improves when SNPs associated with COVID-19 severity are integrated with laboratory and clinical data. Prior to implementation in clinical practice, larger studies in different populations are needed to validate and support the generalization of these results.
Authors
- Sandra Fuentes (ORCID: https://orcid.org/0000-0003-4125-8625)
- Agustín Valido Morales (ORCID: https://orcid.org/0000-0002-8771-7463)
- J.A. Delgado Torralbo
- Flora Sanchez Jimenez
- Antonio León‐Justel (ORCID: https://orcid.org/0000-0002-2219-7129)
- Manuel J. Barragán (ORCID: https://orcid.org/0000-0003-0187-604X)
- Salomón Martín Pérez (ORCID: https://orcid.org/0000-0002-2086-3597)
- Catalina Sánchez Mora
- Jenny A. León (ORCID: https://orcid.org/0000-0001-6236-0878)
- Arrobas Velilla Teresa
Institutions
- Hospital Universitario Virgen Macarena (ES)
- Universidad de Huelva (ES)
Publication Details
- Journal
- Advances in Laboratory Medicine / Avances en Medicina de Laboratorio
- Published
- 2025-05-05
- DOI
- https://doi.org/10.1515/almed-2025-0073
- Citations
- 1
- Primary Topic
- COVID-19 Clinical Research Studies
- Type
- article
- Field-Weighted Citation Impact
- 1.40
Funders
- Instituto de Salud Carlos III
- European Regional Development Fund