Machine learning models for neurocognitive outcome prediction in preterm born infants

Background: Outcome prediction after preterm birth is important for long-term neonatal care, but has proven notoriously challenging for neurocognitive outcome. This study investigated the potential of machine learning to improve neurocognitive outcome prediction at two and five years of corrected age in preterm infants, using readily available predictors from the neonatal setting. Methods: Predictors originating from the antenatal and neonatal period of preterm infants born <30 weeks gestation were used to predict adverse neurocognitive outcome on the Bayley Scale and Wechsler Preschool and Primary Scale of Intelligence. Machine learning models were compared to conventional logistic regression and validated using internal cross-validation. Results: Best performing models were a random forest (two-year outcome) and a support vector machine (five-year outcome) with an area under the receiver operating characteristic curve (AUC) of 0.682 and 0.695 respectively, reaching high negative predictive values (95% and 91%, respectively). These models performed significantly better than the conventional models. Conclusions: The models reached moderate overall predictive performance, yet with promising potential for early identification of children without adverse neurocognitive outcome. Machine learning modestly improved neurocognitive outcome prediction. Future research may harvest the predictive potential of a wider variety of routine (clinical) data, such as vital sign time series.