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dc.contributor.authorWu, Yan-Ting
dc.contributor.authorZhang, Chen-Jie
dc.contributor.authorMol, Ben
dc.contributor.authorKawai, Andrew
dc.contributor.authorLi, Cheng
dc.contributor.authorChen, Lei
dc.contributor.authorWang, Yu
dc.contributor.authorSheng, Jian-Zhong
dc.contributor.authorFan, Jian-Xia
dc.contributor.authorShi, Yi
dc.contributor.authorHuang, He-Feng
dc.date.accessioned2021-06-17T08:57:01Z
dc.date.available2021-06-17T08:57:01Z
dc.date.issued2021-03-31
dc.identifier.citationWu , Y-T , Zhang , C-J , Mol , B , Kawai , A , Li , C , Chen , L , Wang , Y , Sheng , J-Z , Fan , J-X , Shi , Y & Huang , H-F 2021 , ' Early Prediction of Gestational Diabetes Mellitus in the Chinese Population via Advanced Machine Learning ' , Journal of Clinical Endocrinology and Metabolism , vol. 106 , no. 3 , pp. e1191-e1205 . https://doi.org/10.1210/clinem/dgaa899en
dc.identifier.issn0021-972X
dc.identifier.otherPURE: 195427556
dc.identifier.otherPURE UUID: b9f8da79-545a-4170-bc29-6dd45d93aa5f
dc.identifier.otherPubMed: 33351102
dc.identifier.otherPubMedCentral: PMC7947802
dc.identifier.otherScopus: 85102909533
dc.identifier.otherWOS: 000637325400037
dc.identifier.urihttps://hdl.handle.net/2164/16682
dc.descriptionAcknowledgments We thank all those who helped to collect the data and the graduate students who took part in the statistical analysis. Financial Support: This work was supported by the National Key Research and Development Program of China (grant Nos.2018YFC1002804 and 2016YFC1000203), the National Natural Science Foundation of China (grant Nos. 81671412 and 81661128010), Program of Shanghai Academic Research Leader (grant No. 20XD1424100), the Outstanding Youth Medical Talents of Shanghai Rising Stars of Medical Talent Youth Development Program, Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences (grant No. 2019-12M-5-064), the Foundation of Shanghai Municipal Commission of Health and Family Planning (grant No. 20144Y0110), the Natural Science Foundation of Shanghai (grant Nos. 20511101900 and 20ZR1427200), the Shanghai Shenkang Hospital Development Center, the Clinical Technology Innovation Project (grant Nos. SHDC12019107), and the Clinical Skills Improvement Foundation of Shanghai Jiaotong University School of Medicine (grant No. JQ201717).en
dc.format.extent15
dc.language.isoeng
dc.relation.ispartofJournal of Clinical Endocrinology and Metabolismen
dc.rights© The Author(s) 2020. Published by Oxford University Press on behalf of the Endocrine Society. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.comen
dc.subjectSDG 3 - Good Health and Well-beingen
dc.subjectGDMen
dc.subjectearly predictionen
dc.subjectmachine learning modelsen
dc.subjectearly pregnancyen
dc.subjectBMIen
dc.subjectthyroxineen
dc.subjectHEMOGLOBINen
dc.subjectCLASSIFICATIONen
dc.subjectRISKen
dc.subjectPREGNANCYen
dc.subject1STen
dc.subjectDISCRIMINATIONen
dc.subjectGLUCOSEen
dc.subjectINSULIN-RESISTANCEen
dc.subjectINTRAUTERINE EXPOSUREen
dc.subjectASSOCIATIONen
dc.subjectRG Gynecology and obstetricsen
dc.subjectR Medicineen
dc.subjectBiochemistry, medicalen
dc.subjectEndocrinologyen
dc.subjectBiochemistryen
dc.subjectClinical Biochemistryen
dc.subjectEndocrinology, Diabetes and Metabolismen
dc.subject.lccRGen
dc.subject.lccRen
dc.titleEarly Prediction of Gestational Diabetes Mellitus in the Chinese Population via Advanced Machine Learningen
dc.typeJournal articleen
dc.contributor.institutionUniversity of Aberdeen.Other Applied Health Sciencesen
dc.description.statusPeer revieweden
dc.description.versionPublisher PDFen
dc.identifier.doihttps://doi.org/10.1210/clinem/dgaa899
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85102909533&partnerID=8YFLogxKen


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