Predictive Models Using Machine Learning to Identify Fetal Growth Restriction in Patients With Preeclampsia: Development and Evaluation Study
The study successfully developed a model that accurately predicts FGR development in patients with preeclampsia. The SHAP method captures highly relevant risk factors for model interpretation, alleviating concerns about the "black box" problem of ML techniques.
Authors
- Fenglian Shi
- Jing Guo (ORCID: https://orcid.org/0000-0003-2784-3414)
- Xiaoyan You
- Fengchun Yang (ORCID: https://orcid.org/0000-0002-9961-7881)
- Qing Hua (ORCID: https://orcid.org/0009-0000-0175-5059)
- Li Li
- Yadan Zhou
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Zhengzhou Central Hospital (CN)
- Tongji Hospital (CN)
- Huazhong University of Science and Technology (CN)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2025-04-15
- DOI
- https://doi.org/10.2196/70068
- Citations
- 8
- Primary Topic
- Pregnancy and preeclampsia studies
- Type
- article
- Field-Weighted Citation Impact
- 16.46