| Popis: |
BACKGROUND Predicting the outcomes of individual participants for treatment interventions appears central for making mental healthcare more tailored and effective. Machine Learning (ML) has been proven to be able to make such predictions with notable accuracy. However, little work has been done to investigate the performance of such ML-based predictions within digital mental health (DMH) interventions. Using such data from real world settings could provide more realistic estimates of predictive performance in practice. OBJECTIVE This study evaluates the performance of ML in predicting treatment response in a DMH intervention designed for treating depression and anxiety. METHODS Several algorithms were trained based on the data of 970 participants to predict significant reduction in depression and anxiety symptoms, by using clinical and sociodemographic variables. As a Random Forest Classifier (RF) performed best over cross-validation, it was used to predict the outcomes of 279 new participants. RESULTS The RF achieved an accuracy of 0.71 for the test set (base-rate: 0.67, AUC: 0.60, P = .001, balanced accuracy: 0.60). Additionally, predicted non-responders showed less average reduction of their PHQ-9 (-2.7, P = .004) and GAD-7 values (-3.7, P < .001) compared to responders. Besides pre-treatment PHQ and GAD-7 values, the self-reported motivation, type of referral into the program (self versus healthcare provider) as well as Work Productivity and Activity Impairment Questionnaire (WPAI) items contributed most to the predictions. CONCLUSIONS The RF achieved an accuracy of 0.71 for the test set (base-rate: 0.67, AUC: 0.60, P = .001, balanced accuracy: 0.60). Additionally, predicted non-responders showed less average reduction of their PHQ-9 (-2.7, P = .004) and GAD-7 values (-3.7, P < .001) compared to responders. Besides pre-treatment PHQ and GAD-7 values, the self-reported motivation, type of referral into the program (self versus healthcare provider) as well as Work Productivity and Activity Impairment Questionnaire (WPAI) items contributed most to the predictions. CLINICALTRIAL |
