{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T16:13:14Z","timestamp":1788365594389,"version":"build-2803163510"},"reference-count":24,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2021R1C1C100987513"],"award-info":[{"award-number":["2021R1C1C100987513"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS\u20102023\u201000222910"],"award-info":[{"award-number":["RS\u20102023\u201000222910"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS\u20102025\u201000555731"],"award-info":[{"award-number":["RS\u20102025\u201000555731"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003710","name":"Korea Health Industry Development Institute","doi-asserted-by":"publisher","award":["RS\u20102024\u201000403679"],"award-info":[{"award-number":["RS\u20102024\u201000403679"]}],"id":[{"id":"10.13039\/501100003710","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003710","name":"Korea Health Industry Development Institute","doi-asserted-by":"publisher","award":["RS\u20102026\u201025514796"],"award-info":[{"award-number":["RS\u20102026\u201025514796"]}],"id":[{"id":"10.13039\/501100003710","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Diabetes Invest"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aims\/Introduction<\/jats:title>\n                    <jats:p>Gestational diabetes mellitus (GDM) is one of the most frequent pregnancy complications. Investigating clinical risk factors for adverse pregnancy outcomes in women with GDM would help predict and prevent neonatal complications. We developed a machine learning model to discover risk factors for adverse pregnancy outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>\n                      Women with GDM from tertiary hospitals in Korea were included (n\u2009=\u2009305, discovery cohort;\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009911, validation cohort). Supervised machine learning classification models, including ExtraTree, RandomForest, GradientBoosting, AdaBoost, Bagging, XGBoost, and Light Gradient Boosting Machine (LGBM), were developed to predict adverse pregnancy outcomes. Outcomes included large for gestational age (LGA), small for gestational age (SGA), low Apgar score, and preterm delivery. The top\u2010ranked risk factors identified through feature importance were further validated using binary logistic regression analysis.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In predicting LGA, the RandomForest model achieved the highest AUROC of 0.726 on the validation cohort. For SGA, the RandomForest model achieved the highest AUROC of 0.628. For low Apgar score and preterm delivery, the ExtraTree model showed the best performance with AUROCs of 0.689 and 0.616, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study presents machine learning models as a foundational tool for identifying factors associated with adverse pregnancy outcomes in women with GDM. These models may serve as a foundation for future development of clinical decision support tools.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jdi.70364","type":"journal-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T19:37:51Z","timestamp":1782848271000},"page":"1583-1590","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning\u2010based prediction model for adverse pregnancy outcomes in women with gestational diabetes mellitus"],"prefix":"10.1111","volume":"17","author":[{"given":"Jooyeop","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Internal Medicine Seoul National University Bundang Hospital, Seoul National University College of Medicine  Seongnam Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seoung\u2010Ho","family":"Choi","sequence":"additional","affiliation":[{"name":"College of Liberal Arts Faculty of Basic Liberal Art Hansung University  Seoul Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5347-4088","authenticated-orcid":false,"given":"Hyeon Ji","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology Seoul National University Bundang Hospital, Seoul National University College of Medicine  Seongnam Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jee Yoon","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology Seoul National University Bundang Hospital, Seoul National University College of Medicine  Seongnam Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tae Jung","family":"Oh","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine Seoul National University Bundang Hospital, Seoul National University College of Medicine  Seongnam Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sung Hee","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine Seoul National University Bundang Hospital, Seoul National University College of Medicine  Seongnam Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hak Chul","family":"Jang","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine Seoul National University Bundang Hospital, Seoul National University College of Medicine  Seongnam Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1897-0905","authenticated-orcid":false,"given":"Joon Ho","family":"Moon","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine Seoul National University Bundang Hospital, Seoul National University College of Medicine  Seongnam 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