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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| Header | DbId: mdl DbLabel: MEDLINE Ultimate An: 40854813 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An explainable machine learning model in predicting vaginal birth after cesarean section. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Yang+M%22">Yang M</searchLink>; Department of Obstetrics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan, China.; The First Clinical Medical School, Guangdong Medical University, Zhanjiang, China.<br /><searchLink fieldCode="AU" term="%22Long+D%22">Long D</searchLink>; Department of Obstetrics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan, China.; The First Clinical Medical School, Guangdong Medical University, Zhanjiang, China.<br /><searchLink fieldCode="AU" term="%22Li+Y%22">Li Y</searchLink>; Department of Obstetrics, Shenzhen Longhua District Central Hospital, Shenzhen, China.<br /><searchLink fieldCode="AU" term="%22Liu+X%22">Liu X</searchLink>; Department of Critical Care Medicine, Beijing Shijitan Hospital, Capital Medical University, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Bai+Z%22">Bai Z</searchLink>; Department of Obstetrics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan, China.<br /><searchLink fieldCode="AU" term="%22Li+Z%22">Li Z</searchLink>; 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. – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22101136916%22">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</searchLink> [J Matern Fetal Neonatal Med] 2025 Dec; Vol. 38 (1), pp. 2546544. <i>Date of Electronic Publication: </i>2025 Aug 25. – Name: TypePub Label: Publication Type Group: TypPub Data: Journal Article – Name: TitleSource Label: Journal Info Group: Src Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Informa+Healthcare%22">Informa Healthcare </searchLink><i>Country of Publication: </i>England <i>NLM ID: </i>101136916 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1476-4954 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2214764954%22">14764954 </searchLink><i>NLM ISO Abbreviation: </i>J Matern Fetal Neonatal Med <i>Subsets: </i>MEDLINE |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdl&AN=40854813 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/14767058.2025.2546544 Languages: – Code: eng Text: English PhysicalDescription: Pagination: StartPage: 2546544 Titles: – TitleFull: An explainable machine learning model in predicting vaginal birth after cesarean section. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yang M – PersonEntity: Name: NameFull: Long D – PersonEntity: Name: NameFull: Li Y – PersonEntity: Name: NameFull: Liu X – PersonEntity: Name: NameFull: Bai Z – PersonEntity: Name: NameFull: Li Z IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: 2025 Dec Type: published Y: 2025 Identifiers: – Type: issn-electronic Value: 1476-4954 Numbering: – Type: volume Value: 38 – Type: issue Value: 1 Titles: – TitleFull: 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 Type: main |
| ResultId | 1 |
