An explainable machine learning model in predicting vaginal birth after cesarean section.
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| Title: | An explainable machine learning model in predicting vaginal birth after cesarean section. |
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| Authors: | Yang M; Department of Obstetrics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan, China.; The First Clinical Medical School, Guangdong Medical University, Zhanjiang, China., Long D; Department of Obstetrics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan, China.; The First Clinical Medical School, Guangdong Medical University, Zhanjiang, China., Li Y; Department of Obstetrics, Shenzhen Longhua District Central Hospital, Shenzhen, China., Liu X; Department of Critical Care Medicine, Beijing Shijitan Hospital, Capital Medical University, Beijing, China., Bai Z; Department of Obstetrics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan, China., Li Z; The First Clinical Medical School, Guangdong Medical University, Zhanjiang, China.; Department of Obstetrics, The Tenth Affiliated Hospital, Southern Medical University, Dongguan, China.; Dongguan Key Laboratory of Major Diseases in Obstetrics and Gynecology, Dongguan, China. |
| Source: | The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians [J Matern Fetal Neonatal Med] 2025 Dec; Vol. 38 (1), pp. 2546544. Date of Electronic Publication: 2025 Aug 25. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Informa Healthcare Country of Publication: England NLM ID: 101136916 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1476-4954 (Electronic) Linking ISSN: 14764954 NLM ISO Abbreviation: J Matern Fetal Neonatal Med Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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