Internal and external validation of a machine learning algorithm to detect preeclampsia-related adverse outcomes in high-risk pregnancies
The refined ML model maintained high predictive accuracy across two distinct populations, demonstrating its generalizability and potential for integration into clinical decision-making. These findings support the use of machine learning in enhancing the prediction of preeclampsia-related adverse outcomes and improving maternal and neonatal care.
Authors
- Lisa Antonia Lorenz-Meyer
- Sunitha Suresh (ORCID: https://orcid.org/0000-0002-2535-074X)
- Max Hackelöer (ORCID: https://orcid.org/0000-0002-3976-7028)
- W Henrich
- Leon Schmidt
- A. Hoyler
- Oliver Rieger
- Ariel Mueller
- Stefan Verlohren
- Sarosh Rana
- Mark Neznansky
Institutions
- NorthShore University HealthSystem (US)
- Humboldt-Universität zu Berlin (DE)
- University of Chicago (US)
- Massachusetts General Hospital (US)
Publication Details
- Journal
- Pregnancy Hypertension
- Published
- 2026-03-26
- DOI
- https://doi.org/10.1016/j.preghy.2026.101457
- Primary Topic
- Pregnancy and preeclampsia studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00