Predicting therapy outcome in a digital mental health intervention for depression and anxiety: A machine learning approach

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 inter...

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Autores principales:	Silvan Hornstein, Valerie Forman-Hoffman, Albert Nazander, Kristian Ranta, Kevin Hilbert
Formato:	article
Lenguaje:	EN
Publicado:	SAGE Publishing 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/a1c87f1e7a2d4da89785a980cb563b2c
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spelling	oai:doaj.org-article:a1c87f1e7a2d4da89785a980cb563b2c2021-12-01T00:04:44ZPredicting therapy outcome in a digital mental health intervention for depression and anxiety: A machine learning approach2055-207610.1177/20552076211060659https://doaj.org/article/a1c87f1e7a2d4da89785a980cb563b2c2021-11-01T00:00:00Zhttps://doi.org/10.1177/20552076211060659https://doaj.org/toc/2055-2076Objective 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 < 0.001) compared to responders. Besides pre-treatment Patient Health Questionnaire-9 and General Anxiety Disorder Screener-7 values, the self-reported motivation, type of referral into the programme (self vs. healthcare provider) as well as Work Productivity and Activity Impairment Questionnaire items contributed most to the predictions. Conclusions This study provides evidence that social-demographic and clinical variables can be used for machine learning to predict therapy outcomes within the context of a therapist-supported digital mental health intervention. Despite the overall moderate performance, this appears promising as these predictions can potentially improve the outcomes of non-responders by monitoring their progress or by offering alternative or additional treatment.Silvan HornsteinValerie Forman-HoffmanAlbert NazanderKristian RantaKevin HilbertSAGE PublishingarticleComputer applications to medicine. Medical informaticsR858-859.7ENDigital Health, Vol 7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Silvan Hornstein Valerie Forman-Hoffman Albert Nazander Kristian Ranta Kevin Hilbert Predicting therapy outcome in a digital mental health intervention for depression and anxiety: A machine learning approach
description	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 < 0.001) compared to responders. Besides pre-treatment Patient Health Questionnaire-9 and General Anxiety Disorder Screener-7 values, the self-reported motivation, type of referral into the programme (self vs. healthcare provider) as well as Work Productivity and Activity Impairment Questionnaire items contributed most to the predictions. Conclusions This study provides evidence that social-demographic and clinical variables can be used for machine learning to predict therapy outcomes within the context of a therapist-supported digital mental health intervention. Despite the overall moderate performance, this appears promising as these predictions can potentially improve the outcomes of non-responders by monitoring their progress or by offering alternative or additional treatment.
format	article
author	Silvan Hornstein Valerie Forman-Hoffman Albert Nazander Kristian Ranta Kevin Hilbert
author_facet	Silvan Hornstein Valerie Forman-Hoffman Albert Nazander Kristian Ranta Kevin Hilbert
author_sort	Silvan Hornstein
title	Predicting therapy outcome in a digital mental health intervention for depression and anxiety: A machine learning approach
title_short	Predicting therapy outcome in a digital mental health intervention for depression and anxiety: A machine learning approach
title_full	Predicting therapy outcome in a digital mental health intervention for depression and anxiety: A machine learning approach
title_fullStr	Predicting therapy outcome in a digital mental health intervention for depression and anxiety: A machine learning approach
title_full_unstemmed	Predicting therapy outcome in a digital mental health intervention for depression and anxiety: A machine learning approach
title_sort	predicting therapy outcome in a digital mental health intervention for depression and anxiety: a machine learning approach
publisher	SAGE Publishing
publishDate	2021
url	https://doaj.org/article/a1c87f1e7a2d4da89785a980cb563b2c
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Predicting therapy outcome in a digital mental health intervention for depression and anxiety: A machine learning approach

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