A machine-learning approach for decision support and risk stratification of pediatric perioperative patients based on the APRICOT dataset.

This work demonstrates that prediction of patients at low risk of critical PAEs can be made on an individual, rather than population-based, level by using machine learning. Our approach yielded two models that accommodate wide clinical variability and, with further development, are potentially generalizable to many surgical centers.