Abstract: h A method for using a trained machine learning model to predict risk of incident opioid use disorder (OUD) and/or of N an opioid overdose episode for a subject. The method comprises using at least one computer hardware processor to perform: accessing data associated with the subject, wherein the data comprises values for a plurality of predictors; generating input features for the trained machine learning model from the data; and providing the input features as input to the trained machine learning model to obtain an output indicative of the risk of OUD and/or of the opioid overdose episode for the subject, wherein the trained machine learning model comprises a first plurality of values for a respective first plurality of parameters, the first plurality of values used by the at least one computer hardware processor to obtain the output from the input features.