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Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach
BACKGROUND: Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in pred...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7238519/ https://www.ncbi.nlm.nih.gov/pubmed/32429939 http://dx.doi.org/10.1186/s12888-020-02655-4 |
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author | Flygare, Oskar Enander, Jesper Andersson, Erik Ljótsson, Brjánn Ivanov, Volen Z. Mataix-Cols, David Rück, Christian |
author_facet | Flygare, Oskar Enander, Jesper Andersson, Erik Ljótsson, Brjánn Ivanov, Volen Z. Mataix-Cols, David Rück, Christian |
author_sort | Flygare, Oskar |
collection | PubMed |
description | BACKGROUND: Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. METHODS: This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. RESULTS: Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68, 66 and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. CONCLUSIONS: The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT02010619. |
format | Online Article Text |
id | pubmed-7238519 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-72385192020-05-27 Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach Flygare, Oskar Enander, Jesper Andersson, Erik Ljótsson, Brjánn Ivanov, Volen Z. Mataix-Cols, David Rück, Christian BMC Psychiatry Research Article BACKGROUND: Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. METHODS: This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. RESULTS: Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68, 66 and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. CONCLUSIONS: The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT02010619. BioMed Central 2020-05-19 /pmc/articles/PMC7238519/ /pubmed/32429939 http://dx.doi.org/10.1186/s12888-020-02655-4 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Flygare, Oskar Enander, Jesper Andersson, Erik Ljótsson, Brjánn Ivanov, Volen Z. Mataix-Cols, David Rück, Christian Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach |
title | Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach |
title_full | Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach |
title_fullStr | Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach |
title_full_unstemmed | Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach |
title_short | Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach |
title_sort | predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7238519/ https://www.ncbi.nlm.nih.gov/pubmed/32429939 http://dx.doi.org/10.1186/s12888-020-02655-4 |
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