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Machine learning-based prediction model for spontaneous preterm birth in singleton pregnancies using mid-trimester cervical elastographic parameters

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dc.contributor.authorJung, Y.-
dc.contributor.authorHeo, S.-
dc.contributor.authorKwon, H.-
dc.contributor.authorPark, H.-
dc.contributor.authorOh, S.-
dc.contributor.authorSung, J.-
dc.contributor.authorSeol, H.-
dc.contributor.authorKim, H.-
dc.contributor.authorSeong, W.-
dc.contributor.authorHwang, H.-
dc.contributor.authorJung, I.-
dc.contributor.authorKwon, J.-
dc.date.accessioned2024-08-08T08:31:37Z-
dc.date.available2024-08-08T08:31:37Z-
dc.date.issued2023-10-
dc.identifier.issn0960-7692-
dc.identifier.issn1469-0705-
dc.identifier.urihttps://scholarworks.dongguk.edu/handle/sw.dongguk/20634-
dc.format.extent2-
dc.language영어-
dc.language.isoENG-
dc.publisherWILEY-
dc.titleMachine learning-based prediction model for spontaneous preterm birth in singleton pregnancies using mid-trimester cervical elastographic parameters-
dc.typeArticle-
dc.publisher.location미국-
dc.identifier.doi10.1002/uog.26416-
dc.identifier.wosid001150886900096-
dc.identifier.bibliographicCitationUltrasound in Obstetrics & Gynecology, v.62, no.Suppl. 1, pp 34 - 35-
dc.citation.titleUltrasound in Obstetrics & Gynecology-
dc.citation.volume62-
dc.citation.numberSuppl. 1-
dc.citation.startPage34-
dc.citation.endPage35-
dc.type.docTypeMeeting Abstract-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaAcoustics-
dc.relation.journalResearchAreaObstetrics & Gynecology-
dc.relation.journalResearchAreaRadiology, Nuclear Medicine & Medical Imaging-
dc.relation.journalWebOfScienceCategoryAcoustics-
dc.relation.journalWebOfScienceCategoryObstetrics & Gynecology-
dc.relation.journalWebOfScienceCategoryRadiology, Nuclear Medicine & Medical Imaging-
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