Guedalia J, Sompolinsky Y, Persky MN, et al. - Researchers conducted a retrospective electronic‐medical‐record (EMR) based study in order to develop a personalized machine‐learning model for prediction of severe adverse neonatal outcomes (SANO) during the second stage of labor. They assessed a cohort of 73,868 singleton, term deliveries that at the second stage of labor, including 1,346 (1.8%) deliveries with SANO. They analyzed 21 million data points from antepartum features (eg, gravidity and parity) retrieved at admission to the delivery unit, and intrapartum data (eg, cervical dilation and effacement) obtained during the first stage of labor for constructing a gradient boosting model. Outcomes support the utility of data obtained throughout the first stage of labor for predicting SANO during the second stage of labor using a machine learning model. Stratification of parturients at the initiation of the second stage of labor in a "time out" session, can aid in directing a personalized strategy to management of this challenging aspect of labor, as well as improve allocation of staff and resources.