The essential role of electronic fetal monitoring (EFM) during labor is to prevent adverse outcomes due to fetal hypoxia and ischemia. Its established weaknesses include: 1) the obstetrician’s highly subjective visual interpretations of the signal patterns and 2) the widespread use of unproven surrogates for relevant fetal hypoxic and/or ischemic injury such as umbilical arterial pH, intrapartum stillbirth, newborn Apgar scores and neonatal seizures. This technology over the past 50 years has not been shown to decrease stillbirths or reduce the numbers of infants with cerebral palsy. EFM as it is presently used in the clinical setting has been associated with an extraordinary increase in the use of operative vaginal delivery and cesarean delivery. No functional algorithm has yet been developed that integrates clinical data collected in the antepartum period and during labor and any other patient specific data with the results of EFM. The main objective of the proposed research is to use recent breakthroughs in machine learning to drive the development of predictive analytics to support and improve the interpretation of EFM data, especially under real world conditions and in real time where clinicians must make timely decisions about interventions to prevent adverse outcomes. It is anticipated that the proposed research will result in significantly decreased use of operative vaginal delivery and cesarean delivery while more precisely defining the fetus at risk for developing metabolic acidosis and long term neurologic injury.