Prediction of Severe Adverse Neonatal Outcomes at the Second Stage of Labor Using Machine Learning: A Retrospective Cohort Study

Abstract

(BJOG. 2021;128:1824–1832) During the second stage of labor, close maternal and neonatal monitoring is important for avoiding adverse outcomes. Machine learning has been used to analyze complex data patterns and has successfully predicted the need for emergent cesarean delivery (CD) and successful vaginal birth after CD. This study aimed to develop a model to predict adverse neonatal outcomes based on data available to clinicians before the onset of the second stage of labor using machine learning tools.