Predicting cognitive behavioral therapy outcome in the outpatient sector based on clinical routine data: A machine learning approach

Abstract The availability of large-scale datasets and sophisticated machine learning tools enables developing models that predict treatment outcomes for individual patients. However, few studies used routinely available sociodemographic and clinical data for this task, and many previous investigations used highly selected samples. This study aimed to investigate cognitive behavioral therapy (CBT) outcomes in a large, naturalistic and longitudinal dataset. Routine data from a university-based outpatient center with n = 2.147 patients was analyzed. Only baseline data including sociodemographics, symptom measures and functional impairment ratings was used for prediction. Different competing classification and regression models were compared to each other; the best models were then applied to previously unseen validation data. Applied on the validation set, the best performing classification model for remission achieved a balanced accuracy of 59% (p