Machine learning-based prediction algorithm of spontaneous preterm birth using multi-source data
Our results demonstrate that combining EHR data, environmental factors, and ML methods enables highly accurate and moderately precise predictions of sPTB. While the model shows promising discriminatory power, its precision requires improvement before clinical application.
Authors
- Xiya Qin
- Jun Li (ORCID: https://orcid.org/0000-0002-6737-2728)
- Aifen Zhou (ORCID: https://orcid.org/0000-0002-1291-4155)
- Mingzhao Huang (ORCID: https://orcid.org/0000-0001-7244-6835)
- Lianting Hu (ORCID: https://orcid.org/0000-0003-4573-3809)
- X. Lei (ORCID: https://orcid.org/0000-0003-0636-8677)
- Luli Xu
- Xiaoxuan Fan
- Jing Wei (ORCID: https://orcid.org/0000-0002-8803-7056)
- Chao Xiong (ORCID: https://orcid.org/0000-0002-1105-3373)
- Xiaofeng Mu (ORCID: https://orcid.org/0000-0003-0780-0812)
- Kai Chen (ORCID: https://orcid.org/0000-0001-8436-6533)
- Zhiguo Xia
Institutions
- Shanghai Jiao Tong University (CN)
- Wuhan Children's Hospital (CN)
- Earth System Science Interdisciplinary Center (US)
- Huazhong University of Science and Technology (CN)
- University of Maryland, College Park (US)
Publication Details
- Journal
- BMC Pregnancy and Childbirth
- Published
- 2025-12-03
- DOI
- https://doi.org/10.1186/s12884-025-08541-9
- Primary Topic
- Preterm Birth and Chorioamnionitis
- Type
- article
- Field-Weighted Citation Impact
- 0.00