Integrating SHAP analysis with machine learning to predict postpartum hemorrhage in vaginal births.

Journal: BMC pregnancy and childbirth
PMID: 40319253

Abstract

OBJECTIVE: This study aimed to develop a machine learning (ML) model integrated with SHapley Additive exPlanations (SHAP) analysis to predict postpartum hemorrhage (PPH) following vaginal deliveries, offering a potential tool for personalized risk assessment and prevention in clinical settings.

Authors

  • Zixuan Song
    Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China.
  • Hong Lin
    Wenzhou Medical University, Wenzhou, China.
  • Mengyuan Shao
    Department of Obstetrics and Gynecology, Shenyang Women's and Children's Hospital, Shenyang, China.
  • Xiaoxue Wang
    HanZhong Central Hospital, HanZhong, 723000, China.
  • Xueting Chen
    Department of Nephrology, Xinyi people's Hospital, Xuzhou, China.
  • Yangzi Zhou
    Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China.
  • Dandan Zhang
    College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua, China.

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