This paper is in the following e-collection/theme issue:

Diabetes Prevention (77) Diabetes Self-Management (463) Machine Learning (1383) Patient Experiences with Diabetes Technology (28)

Published on in Vol 7, No 3 (2022): Jul-Sep

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/32366, first published .
Machine Learning–Derived Prenatal Predictive Risk Model to Guide Intervention and Prevent the Progression of Gestational Diabetes Mellitus to Type 2 Diabetes: Prediction Model Development Study

Machine Learning–Derived Prenatal Predictive Risk Model to Guide Intervention and Prevent the Progression of Gestational Diabetes Mellitus to Type 2 Diabetes: Prediction Model Development Study

Machine Learning–Derived Prenatal Predictive Risk Model to Guide Intervention and Prevent the Progression of Gestational Diabetes Mellitus to Type 2 Diabetes: Prediction Model Development Study

Authors of this article:

Mukkesh Kumar1, 2, 3 Author Orcid Image ;   Li Ting Ang1, 2 Author Orcid Image ;   Cindy Ho1, 2 Author Orcid Image ;   Shu E Soh4 Author Orcid Image ;   Kok Hian Tan5, 6 Author Orcid Image ;   Jerry Kok Yen Chan7, 8, 9 Author Orcid Image ;   Keith M Godfrey10, 11 Author Orcid Image ;   Shiao-Yng Chan1, 7 Author Orcid Image ;   Yap Seng Chong1, 7 Author Orcid Image ;   Johan G Eriksson1, 7, 12, 13 Author Orcid Image ;   Mengling Feng3, 14 Author Orcid Image ;   Neerja Karnani1, 2, 15 Author Orcid Image
Article Authors Cited by (15) Tweetations (4) Metrics

Mukkesh Kumar   1, 2, 3 , BEng (Hons I) ;   Li Ting Ang   1, 2 , BSc ;   Cindy Ho   1, 2 , BSc ;   Shu E Soh   4 , PhD ;   Kok Hian Tan   5, 6 , MBBS, MMed, MBA ;   Jerry Kok Yen Chan   7, 8, 9 , MB BCh BaO (Hons), PhD ;   Keith M Godfrey   10, 11 , BM in Medicine (Honours & Clinical Distinction), PhD ;   Shiao-Yng Chan   1, 7 , MBBS, PhD ;   Yap Seng Chong   1, 7 , MBBS, MMeD, MD ;   Johan G Eriksson   1, 7, 12, 13 * , MD, DMSc ;   Mengling Feng   3, 14 * , PhD ;   Neerja Karnani   1, 2, 15 * , PhD

1 Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, Singapore

2 Bioinformatics Institute, Agency for Science Technology and Research, Singapore, Singapore

3 Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, Singapore

4 Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

5 Division of Obstetrics and Gynecology, KK Women’s and Children’s Hospital, Singapore, Singapore

6 Obstetrics and Gynecology Academic Clinical Programme, Duke-NUS Graduate Medical School, Singapore, Singapore

7 Department of Obstetrics and Gynaecology and Human Potential Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

8 Department of Reproductive Medicine, KK Women’s and Children’s Hospital, Singapore, Singapore

9 Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore, Singapore

10 MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, United Kingdom

11 National Institute for Health and Care Research Southampton Biomedical Research Centre, University Hospital Southampton NHS Foundation Trust, Southampton, United Kingdom

12 Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland

13 Folkhälsan Research Center, Helsinki, Finland

14 Institute of Data Science, National University of Singapore, Singapore, Singapore

15 Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

*these authors contributed equally

Corresponding Author:

  • Neerja Karnani, PhD
  • Singapore Institute for Clinical Sciences
  • Agency for Science Technology and Research
  • Brenner Centre for Molecular Medicine
  • 30 Medical Drive
  • Singapore, 117609
  • Singapore
  • Phone: 65 64074041
  • Email: neerja_karnani@sics.a-star.edu.sg