Machine Learning prediction of adolescence alcohol use with behavioral/neuro-psychological data

A recently published study entitled “Machine Learning Prediction of Early-Onset Alcohol Use: A Cross-Study, Cross-Sample Validation” (Afzali et al., 2019) focuses on the prediction modeling of early-onset alcohol use in an attempt to highlight important methodological issues and to start a dialogue around best practices in prediction psychiatry. This study addressed five issues in prediction modeling, 1) examination of the comparative performance of different machine-learning algorithms and the potential use of super-learners, 2) feature clustering and domain-contribution analysis 3) coefficient extraction and interpretability, 4) limitations of k-fold cross-validation and necessity of an independent test sample, and 5) ethical concerns regarding the concept of “prediction”. A commentary piece on our work is published here.