Predicting therapy outcome in a digital mental health intervention for depression and anxiety: A machine learning approach
| Autor: | Silvan Hornstein, Valerie Forman-Hoffman, Albert Nazander, Kristian Ranta, Kevin Hilbert |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Digital Health, Vol 7 (2021) |
| Druh dokumentu: | article |
| ISSN: | 2055-2076 20552076 |
| DOI: | 10.1177/20552076211060659 |
| Popis: | Objective Predicting the outcomes of individual participants for treatment interventions appears central to making mental healthcare more tailored and effective. However, little work has been done to investigate the performance of machine learning-based predictions within digital mental health interventions. Therefore, this study evaluates the performance of machine learning in predicting treatment response in a digital mental health intervention designed for treating depression and anxiety. Methods Several algorithms were trained based on the data of 970 participants to predict a significant reduction in depression and anxiety symptoms using clinical and sociodemographic variables. As a random forest classifier performed best over cross-validation, it was used to predict the outcomes of 279 new participants. Results The random forest achieved an accuracy of 0.71 for the test set (base rate: 0.67, area under curve (AUC): 0.60, p = 0.001, balanced accuracy: 0.60). Additionally, predicted non-responders showed less average reduction of their Patient Health Questionnaire-9 (PHQ-9) (−2.7, p = 0.004) and General Anxiety Disorder Screener-7 values (−3.7, p |
| Databáze: | Directory of Open Access Journals |
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