Applying Automated Machine Learning to Predict Mode of Delivery Using Ongoing Intrapartum Data in Laboring Patients.

Journal: American journal of perinatology
Published Date: May 1, 2024

Abstract

OBJECTIVE: This study aimed to develop and validate a machine learning (ML) model to predict the probability of a vaginal delivery (Partometer) using data iteratively obtained during labor from the electronic health record.

Authors

  • Melissa S Wong
    Department of Obstetrics and Gynecology, Cedars-Sinai Medical Center, Los Angeles, California.
  • Matthew Wells
    Enterprise Data Intelligence, Cedars-Sinai Medical Center, Los Angeles, California.
  • Davina Zamanzadeh
  • Samir Akre
    Medical and Imaging Informatics (MII) Group, Department of Radiological Sciences, University of California Los Angeles, Los Angeles, California.
  • Joshua M Pevnick
    Division of Informatics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
  • Alex A T Bui
  • Kimberly D Gregory
    Department of Obstetrics and Gynecology, Cedars-Sinai Medical Center, Los Angeles, California.

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