Dynamic machine learning models for predicting cesarean delivery risk in women with no prior cesarean delivery: A retrospective nationwide cohort analysis.

Journal: International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
Published Date: May 22, 2025

Abstract

OBJECTIVE: To develop and validate advanced machine learning (ML) models for predicting unplanned intrapartum cesarean deliveries in women with no previous cesarean delivery, using both static and dynamic clinical data.

Authors

  • Ido Givon
    Helen Schneider Hospital for Women, Rabin Medical Center, Petach Tikva, Israel.
  • Nati Bor
    Helen Schneider Hospital for Women, Rabin Medical Center, Petach Tikva, Israel.
  • Ran Matot
    Helen Schneider Hospital for Women, Rabin Medical Center, Petach Tikva, Israel.
  • Lior Friedrich
    Holon Institute of Technology, Holon, Israel.
  • Daya Gross
    Holon Institute of Technology, Holon, Israel.
  • Gili Konforty
    Holon Institute of Technology, Holon, Israel.
  • Arriel Benis
    Faculty of Industrial Engineering and Technology Management, Holon Institute of Technology, Holon, Israel.
  • Eran Hadar
    Helen Schneider Hospital for Women, Rabin Medical Center, Petach Tikva, Israel.

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