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Nonalcoholic fatty liver disease and early prediction of gestational diabetes mellitus using machine learning methods
DC Field | Value | Language |
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dc.contributor.author | Lee, Seung Mi | - |
dc.contributor.author | Hwangbo, Suhyun | - |
dc.contributor.author | Norwitz, Errol R. | - |
dc.contributor.author | Koo, Ja Nam | - |
dc.contributor.author | Oh, Ig Hwan | - |
dc.contributor.author | Choi, Eun Saem | - |
dc.contributor.author | Jung, Young Mi | - |
dc.contributor.author | Kim, Sun Min | - |
dc.contributor.author | Kim, Byoung Jae | - |
dc.contributor.author | Kim, Sang Youn | - |
dc.contributor.author | Kim, Gyoung Min | - |
dc.contributor.author | Kim, Won | - |
dc.contributor.author | Joo, Sae Kyung | - |
dc.contributor.author | Shin, Sue | - |
dc.contributor.author | Park, Chan-Wook | - |
dc.contributor.author | Park, Taesung | - |
dc.contributor.author | Park, Joong Shin | - |
dc.date.accessioned | 2022-05-04T02:06:53Z | - |
dc.date.available | 2022-05-04T02:06:53Z | - |
dc.date.created | 2022-01-17 | - |
dc.date.issued | 2022-01-01 | - |
dc.identifier.citation | Clinical and Molecular Hepatology, Vol.28 No.1, pp.105-116 | - |
dc.identifier.issn | 2287-2728 | - |
dc.identifier.uri | https://hdl.handle.net/10371/179511 | - |
dc.description.abstract | Background/Aims: To develop an early prediction model for gestational diabetes mellitus (GDM) using machine learning and to evaluate whether the inclusion of nonalcoholic fatty liver disease (NAFLD)-associated variables increases the performance of model. Methods: This prospective cohort study evaluated pregnant women for NAFLD using ultrasound at 10-14 weeks and screened them for GDM at 24-28 weeks of gestation. The clinical variables before 14 weeks were used to develop prediction models for GDM (setting 1, conventional risk factors; setting 2, addition of new risk factors in recent guidelines; setting 3, addition of routine clinical variables; setting 4, addition of NALFD-associated variables, including the presence of NAFLD and laboratory results; and setting 5, top 11 variables identified from a stepwise variable selection method). The predictive models were constructed using machine learning methods, including logistic regression, random forest, support vector machine, and deep neural networks. Results: Among 1,443 women, 86 (6.0%) were diagnosed with GDM. The highest performing prediction model among settings 1-4 was setting 4, which included both clinical and NAFLD-associated variables (area under the receiver operating characteristic curve [AUC] 0.563-0.697 in settings 1-3 vs. 0.740-0.781 in setting 4). Setting 5, with top 11 variables (which included NAFLD and hepatic steatosis index), showed similar predictive power to setting 4 (AUC 0.719-0.819 in setting 5, P=not significant between settings 4 and 5). Conclusions: We developed an early prediction model for GDM using machine learning. The inclusion of NAFLD-associated variables significantly improved the performance of GDM prediction. | - |
dc.language | 영어 | - |
dc.publisher | 대한간학회 | - |
dc.title | Nonalcoholic fatty liver disease and early prediction of gestational diabetes mellitus using machine learning methods | - |
dc.type | Article | - |
dc.identifier.doi | 10.3350/cmh.2021.0174 | - |
dc.citation.journaltitle | Clinical and Molecular Hepatology | - |
dc.identifier.wosid | 000740510200010 | - |
dc.identifier.scopusid | 2-s2.0-85123263538 | - |
dc.citation.endpage | 116 | - |
dc.citation.number | 1 | - |
dc.citation.startpage | 105 | - |
dc.citation.volume | 28 | - |
dc.identifier.kciid | ART002789211 | - |
dc.description.isOpenAccess | Y | - |
dc.contributor.affiliatedAuthor | Park, Taesung | - |
dc.contributor.affiliatedAuthor | Park, Joong Shin | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
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