Prediction of spontaneous preterm birth in pregnant women using machine learning
The machine learning model may help identify pregnant women at high risk of sPTB, and individual risk factor analysis could provide reference for clinical decision. However, as some key variables are not part of routine laboratory tests during pregnancy worldwide, the model's generalizability and clinical applicability require further study.
Authors
- Xinru Gao
- Kun‐Lin Yang (ORCID: https://orcid.org/0000-0002-7958-9334)
- Shuai Wang (ORCID: https://orcid.org/0000-0003-1595-9858)
- Zhibin Li (ORCID: https://orcid.org/0009-0005-1393-4800)
- Xiaoxue Yang (ORCID: https://orcid.org/0000-0003-0237-2492)
- Rong Qiang (ORCID: https://orcid.org/0009-0004-3716-8680)
- Xuewu Song (ORCID: https://orcid.org/0000-0003-4750-7151)
- Simin Zhang (ORCID: https://orcid.org/0000-0003-3720-486X)
- Peng Gao (ORCID: https://orcid.org/0000-0003-4336-2899)
Institutions
- University of Electronic Science and Technology of China (CN)
- Northwest Women's and Children's Hospital (CN)
Publication Details
- Journal
- Archives of Gynecology and Obstetrics
- Published
- 2025-07-12
- DOI
- https://doi.org/10.1007/s00404-025-08117-0
- Citations
- 2
- Primary Topic
- Preterm Birth and Chorioamnionitis
- Type
- article
- Field-Weighted Citation Impact
- 2.71
Funders
- Yakult Bio-Science Foundation
- Key Research and Development Projects of Shaanxi Province