With machine learning, Mayo Clinic researchers found it is possible to predict how patterns of changes in pregnant patients who are in labor can help identify whether a vaginal delivery will occur with good outcomes for mom and baby. WHY IT MATTERS The ability to change the shape of and open the birth canal to make way for a baby to be born varies from patient to patient. When obstetricians analyze contractions, as well as fetal heartbeats, they assess the progress of labor and make recommendations on levels of care for the medically-risky delivery process of birth. Mayo Clinic researchers say these new models can predict a composite of medical outcomes and the probability of poor labor outcomes – cesarean delivery in active labor, postpartum hemorrhage, intra-amniotic infection, shoulder dystocia, neonatal morbidity and mortality – based on what machine learning can do with dilation data. Use of the models could result in more individualized clinical decisions using the baseline characteristics of each patient, and they could also be a tool to help remote physicians and midwives transfer rural or remote patients to the appropriate level of care, said Dr. Abimbola Famuyide, a Mayo Clinic OB-GYN and senior author of the study in a prepared statement.