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Development of early prediction model for pregnancy-associated hypertension with graph-based semi-supervised learning.
Lee, Seung Mi; Nam, Yonghyun; Choi, Eun Saem; Jung, Young Mi; Sriram, Vivek; Leiby, Jacob S; Koo, Ja Nam; Oh, Ig Hwan; Kim, Byoung Jae; Kim, Sun Min; Kim, Sang Youn; Kim, Gyoung Min; Joo, Sae Kyung; Shin, Sue; Norwitz, Errol R; Park, Chan-Wook; Jun, Jong Kwan; Kim, Won; Kim, Dokyoon; Park, Joong Shin.
Affiliation
  • Lee SM; Department of Obstetrics and Gynecology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
  • Nam Y; Department of Biostatistics, Epidemiology and Informatics, The Perelman School of Medicine, University of Pennsylvania, B304 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, 19104-6116, USA.
  • Choi ES; Department of Obstetrics and Gynecology, Seoul National University Hospital, Seoul, South Korea.
  • Jung YM; Department of Biostatistics, Epidemiology and Informatics, The Perelman School of Medicine, University of Pennsylvania, B304 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, 19104-6116, USA.
  • Sriram V; Department of Obstetrics and Gynecology, Seoul National University Hospital, Seoul, South Korea.
  • Leiby JS; Department of Obstetrics and Gynecology, Korea University College of Medicine, Seoul, South Korea.
  • Koo JN; Department of Obstetrics and Gynecology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
  • Oh IH; Department of Obstetrics and Gynecology, Seoul National University Hospital, Seoul, South Korea.
  • Kim BJ; Department of Biostatistics, Epidemiology and Informatics, The Perelman School of Medicine, University of Pennsylvania, B304 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, 19104-6116, USA.
  • Kim SM; Department of Biostatistics, Epidemiology and Informatics, The Perelman School of Medicine, University of Pennsylvania, B304 Richards Building, 3700 Hamilton Walk, Philadelphia, PA, 19104-6116, USA.
  • Kim SY; Seoul Women's Hospital, Incheon, South Korea.
  • Kim GM; Seoul Women's Hospital, Incheon, South Korea.
  • Joo SK; Department of Obstetrics and Gynecology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
  • Shin S; Department of Obstetrics and Gynecology, Seoul Metropolitan Government, Seoul National University Boramae Medical Center, Seoul, South Korea.
  • Norwitz ER; Department of Obstetrics and Gynecology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
  • Park CW; Department of Obstetrics and Gynecology, Seoul Metropolitan Government, Seoul National University Boramae Medical Center, Seoul, South Korea.
  • Jun JK; Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea.
  • Kim W; Department of Radiology, Yonsei University College of Medicine, Seoul, South Korea.
  • Kim D; Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea.
  • Park JS; Department of Internal Medicine, Seoul Metropolitan Government, Seoul National University Boramae Medical Center, Seoul, South Korea.
Sci Rep ; 12(1): 15793, 2022 09 22.
Article in En | MEDLINE | ID: mdl-36138035
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Hypertension, Pregnancy-Induced / Supervised Machine Learning Type of study: Diagnostic_studies / Guideline / Observational_studies / Prognostic_studies / Risk_factors_studies Limits: Female / Humans / Pregnancy Language: En Journal: Sci Rep Year: 2022 Document type: Article Affiliation country:
Fulltext
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Hypertension, Pregnancy-Induced / Supervised Machine Learning Type of study: Diagnostic_studies / Guideline / Observational_studies / Prognostic_studies / Risk_factors_studies Limits: Female / Humans / Pregnancy Language: En Journal: Sci Rep Year: 2022 Document type: Article Affiliation country: