Prediction of spontaneous preterm birth using supervised machine learning on metabolomic data: A case-cohort study.

Journal: BJOG : an international journal of obstetrics and gynaecology
Published Date: Nov 20, 2023

Abstract

OBJECTIVES: To identify and internally validate metabolites predictive of spontaneous preterm birth (sPTB) using multiple machine learning methods and sequential maternal serum samples, and to predict spontaneous early term birth (sETB) using these metabolites.

Authors

  • Yasmina Al Ghadban
    Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK.
  • Yuheng Du
    Department of Biostatistics, School of Public Health, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 49109, USA.
  • D Stephen Charnock-Jones
    Department of Obstetrics and Gynaecology, University of Cambridge, Cambridge, UK.
  • Lana X Garmire
    Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii. lgarmire@cc.hawaii.edu.
  • Gordon C S Smith
    Department of Obstetrics and Gynaecology, University of Cambridge, Cambridge, UK.
  • Ulla Sovio
    Department of Obstetrics and Gynaecology, University of Cambridge, Cambridge, UK.

Keywords

External Resources

Popular Topics
Recent Journals