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Automated Machine Learning (AutoML)-Derived Preconception Predictive Risk Model to Guide Early Intervention for Gestational Diabetes Mellitus.
Kumar, Mukkesh; Ang, Li Ting; Png, Hang; Ng, Maisie; Tan, Karen; Loy, See Ling; Tan, Kok Hian; Chan, Jerry Kok Yen; Godfrey, Keith M; Chan, Shiao-Yng; Chong, Yap Seng; Eriksson, Johan G; Feng, Mengling; Karnani, Neerja.
Afiliação
  • Kumar M; Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore 138632, Singapore.
  • Ang LT; Bioinformatics Institute, Agency for Science Technology and Research, Singapore 138632, Singapore.
  • Png H; Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore 119077, Singapore.
  • Ng M; Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore 138632, Singapore.
  • Tan K; Bioinformatics Institute, Agency for Science Technology and Research, Singapore 138632, Singapore.
  • Loy SL; Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore 138632, Singapore.
  • Tan KH; Bioinformatics Institute, Agency for Science Technology and Research, Singapore 138632, Singapore.
  • Chan JKY; Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore 138632, Singapore.
  • Godfrey KM; Bioinformatics Institute, Agency for Science Technology and Research, Singapore 138632, Singapore.
  • Chan SY; Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore 138632, Singapore.
  • Chong YS; Obstetrics & Gynaecology Academic Clinical Programme, Duke-NUS Medical School, Singapore 169857, Singapore.
  • Eriksson JG; Department of Reproductive Medicine, KK Women's and Children's Hospital, Singapore 229899, Singapore.
  • Feng M; Obstetrics & Gynaecology Academic Clinical Programme, Duke-NUS Medical School, Singapore 169857, Singapore.
  • Karnani N; Division of Obstetrics and Gynecology, KK Women's and Children's Hospital, Singapore 229899, Singapore.
Int J Environ Res Public Health ; 19(11)2022 06 01.
Article em En | MEDLINE | ID: mdl-35682375
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Texto completo: 1 Coleções: 01-internacional Contexto em Saúde: 1_ASSA2030 / 2_ODS3 Base de dados: MEDLINE Assunto principal: Diabetes Gestacional / Nascimento Prematuro / Diabetes Mellitus Tipo 2 Tipo de estudo: Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Child / Female / Humans / Newborn / Pregnancy Idioma: En Revista: Int J Environ Res Public Health Ano de publicação: 2022 Tipo de documento: Article
Texto completo
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XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Contexto em Saúde: 1_ASSA2030 / 2_ODS3 Base de dados: MEDLINE Assunto principal: Diabetes Gestacional / Nascimento Prematuro / Diabetes Mellitus Tipo 2 Tipo de estudo: Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Child / Female / Humans / Newborn / Pregnancy Idioma: En Revista: Int J Environ Res Public Health Ano de publicação: 2022 Tipo de documento: Article