Machine learning‐based prediction of postpartum hemorrhage using maternal clinical and biochemical features
Machine learning models can reliably identify patients at risk for postpartum hemorrhage and its complications using accessible pre-delivery data. The robustness of reduced-variable models enhances their clinical utility, especially in resource-limited settings. Integration of such algorithms into electronic health record systems might support early intervention and improved maternal outcomes.
Authors
- Nazlı Orhan (ORCID: https://orcid.org/0000-0003-3974-9417)
- Uğurcan Zorlu (ORCID: https://orcid.org/0000-0002-8912-0812)
- Hasan Baki Altınsoy (ORCID: https://orcid.org/0000-0003-0934-3600)
- Atakan Tanaçan (ORCID: https://orcid.org/0000-0001-8209-8248)
- Gülcan Okutucu (ORCID: https://orcid.org/0000-0003-4618-8312)
- Dilek ŞAHİN (ORCID: https://orcid.org/0000-0003-0865-7763)
Institutions
- Ankara University (TR)
- Bilkent University (TR)
- Sağlık Bilimleri Üniversitesi (TR)
Publication Details
- Journal
- International Journal of Gynecology & Obstetrics
- Published
- 2025-10-27
- DOI
- https://doi.org/10.1002/ijgo.70609
- Primary Topic
- Maternal and fetal healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00