The Value of Oxygenation Vital Signs in Machine Learning Prediction of Neurodevelopmental Outcomes in Preterm Infants

Machine learning models predicting neurodevelopmental outcome in preterm infants have great potential, but have often relied on inaccessible brain magnetic resonance imaging measurements. This study aimed to build models using readily available clinical predictors and investigate the potential of vital sign data in the prediction of neurodevelopmental outcome after preterm birth. Readily available predictors from the antenatal and neonatal period of preterm infants born <30 weeks gestation were combined with vital sign data from the first seven days after birth to predict motor and cognitive outcome at two and five years corrected age. A conventional logistic regression models was compared with a support vector machine and a neural network. Vital sign times series were investigated using two approaches; basic descriptives of vital sign data were compared to an advanced approach in which vital sign time series were processed in an auto-encoder and longshort-term-memory network. Best performing models reached moderate area under the receiver operating characteristic curves (0.592 to 0.703), yet reaching high negative predictive values (85% to 94%). Vital sign data did modestly improve prediction of motor outcome, but not prediction of cognitive outcome. Advanced handling of vital sign time series did not improve prediction above basic descriptives of vital signs. Neurodevelopmental outcome prediction on routine clinical data remains challenging, but also shows potential in the identification of infants with low risk of adverse outcome. Future work may take advantage of higher resolution and a wider variety of vital signs.