Development and validation of a machine learning model for predicting intrapartum fever using pre-labor analgesia clinical indicators: a multicenter retrospective study
The multilayer perceptron model, utilizing readily available pre-labor analgesia variables, demonstrates potential for predicting intrapartum fever. In comparison to existing tools, this model may enable earlier identification of high-risk parturients, supporting timely interventions and potentially enhancing maternal and neonatal health outcomes.
Authors
- Ling Liang (ORCID: https://orcid.org/0000-0002-3115-2563)
- Mengqiao Wang (ORCID: https://orcid.org/0000-0002-3391-7507)
- Yuanling Li
- Gang Zhang (ORCID: https://orcid.org/0000-0003-3711-7870)
- Bo Liu (ORCID: https://orcid.org/0000-0003-2455-2021)
- Jia Fei (ORCID: https://orcid.org/0000-0002-6650-7870)
- Chunping Li (ORCID: https://orcid.org/0000-0002-9126-4225)
- Jiän Zhang (ORCID: https://orcid.org/0000-0002-0557-886X)
- Huiru Li (ORCID: https://orcid.org/0009-0001-9921-8928)
- Dayuan Wei
- Hongquan Xiao
Institutions
- Chengdu Medical College (CN)
- Chengdu Women's and Children's Central Hospital (CN)
- Maternal and Child Health Hospital of Sichuan Province (CN)
Publication Details
- Journal
- BMC Pregnancy and Childbirth
- Published
- 2025-03-06
- DOI
- https://doi.org/10.1186/s12884-025-07203-0
- Citations
- 5
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 6.77