Select or adjust? How information from early treatment stages boosts the prediction of non-response in Internet-based depression treatment

The present work compares algorithms using features gathered at baseline or early in treatment in their capability to predict non-response to a 6-week online program targeting depression. Our training and test sample encompassed 1270 and 318 individuals, respectively. We trained Random Forest Algorithms on self-report and process features gathered at baseline and after 2 weeks of treatment. The best performances were reached by our models involving early treatment characteristics (recall: 0.75-0.76; AUC: 0.71-0.77). Models trained on baseline data only were not significantly better than our benchmark. In-treatment adaptation, instead of a priori selection, might constitute a more feasible approach for improving response when relying on easily accessible self-report features. notReviewed other