Quantitative prediction of postpartum hemorrhage in cesarean section on machine learning.

Journal: BMC medical informatics and decision making
PMID: 38872184

Abstract

BACKGROUND: Cesarean section-induced postpartum hemorrhage (PPH) potentially causes anemia and hypovolemic shock in pregnant women. Hence, it is helpful for obstetricians and anesthesiologists to prepare pre-emptive prevention when predicting PPH occurrence in advance. However, current works on PPH prediction focus on whether PPH occurs rather than assessing PPH amount. To this end, this work studies quantitative PPH prediction with machine learning (ML).

Authors

  • Meng Wang
    State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150001, China.
  • Gao Yi
    Shijiazhuang Obstetrics and Gynecology Hospital, Shijiazhuang, 050000, China.
  • Yunjia Zhang
    School of Information Engineering, China University of Geosciences, Beijing, 100083, China.
  • Mei Li
    Department of Laboratory Medicine, Med+X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China.
  • Jin Zhang
    Department of Otolaryngology, The Second People's Hospital of Yibin, Yibin, Sichuan, China.

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