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

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第一作者： Meng,Wang

第一单位： School of Information Engineering, China University of Geosciences, Beijing, 100083, China.;Tangshan Polytechnic College, Tangshan, 063299, China.

作者： Meng,Wang ^[1] ; Gao,Yi ^[2] ; Yunjia,Zhang ^[3] ; Mei,Li ^[4] ; Jin,Zhang ^[5]

作者单位： School of Information Engineering, China University of Geosciences, Beijing, 100083, China.;Tangshan Polytechnic College, Tangshan, 063299, China. ^[1] Shijiazhuang Obstetrics and Gynecology Hospital, Shijiazhuang, 050000, China. ^[2] School of Information Engineering, China University of Geosciences, Beijing, 100083, China. ^[3] School of Information Engineering, China University of Geosciences, Beijing, 100083, China. maggieli@cugb.edu.cn. ^[4] Shijiazhuang Obstetrics and Gynecology Hospital, Shijiazhuang, 050000, China. morningzhangj@163.com. ^[5]

关键词 Cesarean section Machine learning Partial dependence plot Permutation importance Postpartum hemorrhage Random forest Regression

医学主题词人类(Humans);女(雌)性(Female);剖宫产术(Cesarean Section);产后出血(Postpartum Hemorrhage);妊娠(Pregnancy);成年人(Adult)

DOI 10.1186/s12911-024-02571-7

PMID 38872184

发布时间 2024-06-16

基金项目

22377766D/critical research and development program of Hebei Province 22377766D/critical research and development program of Hebei Province 22377766D/critical research and development program of Hebei Province 22377766D/critical research and development program of Hebei Province 22377766D/critical research and development program of Hebei Province

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