Prediction of postpartum depression in women: development and validation of multiple machine learning models
The LR and ANN models achieved the best predictive performances. Applying these models and risk assessment tools to early predict and screen PPD has several implications for public health.
Authors
- Jinfan Zuo
- Weijing Qi
- Sha Huang (ORCID: https://orcid.org/0000-0003-0677-5926)
- Haoyu Jin
- Xin Cui (ORCID: https://orcid.org/0000-0002-4277-7972)
- Cong Li (ORCID: https://orcid.org/0000-0002-7637-5920)
- Ziqi Wei (ORCID: https://orcid.org/0000-0001-9402-8386)
- Qing Guo (ORCID: https://orcid.org/0000-0003-0522-1243)
- Jie Hu (ORCID: https://orcid.org/0000-0003-4660-4056)
- Yipeng Wang (ORCID: https://orcid.org/0000-0001-9769-9839)
- Yongjian Wang (ORCID: https://orcid.org/0000-0001-5893-7996)
Institutions
- Hebei Medical University (CN)
- Tianjin Medical University Cancer Institute and Hospital (CN)
- First Hospital of Shijiazhuang (CN)
Publication Details
- Journal
- Journal of Translational Medicine
- Published
- 2025-03-07
- DOI
- https://doi.org/10.1186/s12967-025-06289-6
- Citations
- 24
- Primary Topic
- Maternal Mental Health During Pregnancy and Postpartum
- Type
- article
- Field-Weighted Citation Impact
- 45.09
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Hebei Province