Dynamic machine learning models for predicting cesarean delivery risk in women with no prior cesarean delivery: A retrospective nationwide cohort analysis
These ML models provide an innovative approach to predicting unplanned cesarean delivery by integrating diverse clinical parameters, enhancing decision making, and optimizing labor management. Prospective validation and seamless integration into clinical workflows are required to establish their utility in broader obstetric practice.
Authors
- Lior Friedrich (ORCID: https://orcid.org/0000-0002-4840-363X)
- Ran Matot (ORCID: https://orcid.org/0000-0002-3237-1696)
- Eran Hadar (ORCID: https://orcid.org/0000-0002-3101-2827)
- Diane Gross
- Nati Bor (ORCID: https://orcid.org/0009-0005-2698-5586)
- Ido Givon
- Arriel Benis (ORCID: https://orcid.org/0000-0002-9125-8300)
- Gili Konforty
Institutions
- Holon Institute of Technology (IL)
- Tel Aviv University (IL)
- Rabin Medical Center (IL)
Publication Details
- Journal
- International Journal of Gynecology & Obstetrics
- Published
- 2025-05-22
- DOI
- https://doi.org/10.1002/ijgo.70234
- Citations
- 1
- Primary Topic
- Maternal and Perinatal Health Interventions
- Type
- article
- Field-Weighted Citation Impact
- 2.06