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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 inte...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8637697/ https://www.ncbi.nlm.nih.gov/pubmed/34868624 http://dx.doi.org/10.1177/20552076211060659 |
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author | Hornstein, Silvan Forman-Hoffman, Valerie Nazander, Albert Ranta, Kristian Hilbert, Kevin |
author_facet | Hornstein, Silvan Forman-Hoffman, Valerie Nazander, Albert Ranta, Kristian Hilbert, Kevin |
author_sort | Hornstein, Silvan |
collection | PubMed |
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 | Online Article Text |
id | pubmed-8637697 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-86376972021-12-03 Predicting therapy outcome in a digital mental health intervention for depression and anxiety: A machine learning approach Hornstein, Silvan Forman-Hoffman, Valerie Nazander, Albert Ranta, Kristian Hilbert, Kevin Digit Health Original Research 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. SAGE Publications 2021-11-29 /pmc/articles/PMC8637697/ /pubmed/34868624 http://dx.doi.org/10.1177/20552076211060659 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Original Research Hornstein, Silvan Forman-Hoffman, Valerie Nazander, Albert Ranta, Kristian Hilbert, Kevin Predicting therapy outcome in a digital mental health intervention for depression and anxiety: A machine learning approach |
title | 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_short | 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 |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8637697/ https://www.ncbi.nlm.nih.gov/pubmed/34868624 http://dx.doi.org/10.1177/20552076211060659 |
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