Predictive modeling of complications arising from early-onset preeclampsia in pregnant women
This study introduces a novel approach to risk stratification in early-onset preeclampsia, integrating baseline and delivery data within a machine learning framework. Our results demonstrate that refined risk prediction with a minimal number of variables can complement existing clinical tools. Further validation in larger cohorts is needed to confirm its potential impact on decision-making and maternal outcomes.
Authors
- Cecilia Villalaín (ORCID: https://orcid.org/0000-0002-9456-4100)
- Alberto Galindo (ORCID: https://orcid.org/0000-0002-1308-1474)
- José L. Ayala (ORCID: https://orcid.org/0000-0001-7236-5330)
- Ignacio Herraı̀z (ORCID: https://orcid.org/0000-0001-6807-4944)
- Paula Domínguez-del Olmo (ORCID: https://orcid.org/0000-0003-2169-6011)
Institutions
- Universidad Complutense de Madrid (ES)
- Research Institute Hospital 12 de Octubre (ES)
- Spanish Clinical Research Network (ES)
- Research Network (United States) (US)
Publication Details
- Journal
- Women s Health
- Published
- 2025-07-21
- DOI
- https://doi.org/10.1177/17455057251348978
- Primary Topic
- Pregnancy and preeclampsia studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Instituto de Salud Carlos III