Developing a Machine Learning Model for Predicting Postnatal Growth in Very Low Birth Weight Infants

Objective of the work is the development of prognostic machine learning models that predict qualitative and quantitative measures of postnatal growth in very low birth weight preterm infants. Observational retrospective data about 964 infants at risk are retrieved from "Fondazione Monza e Brianza per it bambino e la mamma"'s electronic medical record. Both prenatal (gestational, socioeconomic, etc.) and perinatal (nutritional, respiratory assistance, drug prescription and daily growth) data up to a week after birth are the features included. Model's performances are compared to previous literature and human performance, showing a substantial improvement (in e.g., best regreccion MAE=0.49, best classification AUC=0.94).