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Predicting therapy success for treatment as usual and blended treatment in the domain of depression

In this paper, we explore the potential of predicting therapy success for patients in mental health care. Such predictions can eventually improve the process of matching effective therapy types to individuals. In the EU project E-COMPARED, a variety of information is gathered about patients sufferin...

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Autores principales: van Breda, Ward, Bremer, Vincent, Becker, Dennis, Hoogendoorn, Mark, Funk, Burkhardt, Ruwaard, Jeroen, Riper, Heleen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5945603/
https://www.ncbi.nlm.nih.gov/pubmed/29862165
http://dx.doi.org/10.1016/j.invent.2017.08.003
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author van Breda, Ward
Bremer, Vincent
Becker, Dennis
Hoogendoorn, Mark
Funk, Burkhardt
Ruwaard, Jeroen
Riper, Heleen
author_facet van Breda, Ward
Bremer, Vincent
Becker, Dennis
Hoogendoorn, Mark
Funk, Burkhardt
Ruwaard, Jeroen
Riper, Heleen
author_sort van Breda, Ward
collection PubMed
description In this paper, we explore the potential of predicting therapy success for patients in mental health care. Such predictions can eventually improve the process of matching effective therapy types to individuals. In the EU project E-COMPARED, a variety of information is gathered about patients suffering from depression. We use this data, where 276 patients received treatment as usual and 227 received blended treatment, to investigate to what extent we are able to predict therapy success. We utilize different encoding strategies for preprocessing, varying feature selection techniques, and different statistical procedures for this purpose. Significant predictive power is found with average AUC values up to 0.7628 for treatment as usual and 0.7765 for blended treatment. Adding daily assessment data for blended treatment does currently not add predictive accuracy. Cost effectiveness analysis is needed to determine the added potential for real-world applications.
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spelling pubmed-59456032018-06-01 Predicting therapy success for treatment as usual and blended treatment in the domain of depression van Breda, Ward Bremer, Vincent Becker, Dennis Hoogendoorn, Mark Funk, Burkhardt Ruwaard, Jeroen Riper, Heleen Internet Interv Special issue for the ISRII 2017 meeting In this paper, we explore the potential of predicting therapy success for patients in mental health care. Such predictions can eventually improve the process of matching effective therapy types to individuals. In the EU project E-COMPARED, a variety of information is gathered about patients suffering from depression. We use this data, where 276 patients received treatment as usual and 227 received blended treatment, to investigate to what extent we are able to predict therapy success. We utilize different encoding strategies for preprocessing, varying feature selection techniques, and different statistical procedures for this purpose. Significant predictive power is found with average AUC values up to 0.7628 for treatment as usual and 0.7765 for blended treatment. Adding daily assessment data for blended treatment does currently not add predictive accuracy. Cost effectiveness analysis is needed to determine the added potential for real-world applications. Elsevier 2017-09-06 /pmc/articles/PMC5945603/ /pubmed/29862165 http://dx.doi.org/10.1016/j.invent.2017.08.003 Text en © 2017 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Special issue for the ISRII 2017 meeting
van Breda, Ward
Bremer, Vincent
Becker, Dennis
Hoogendoorn, Mark
Funk, Burkhardt
Ruwaard, Jeroen
Riper, Heleen
Predicting therapy success for treatment as usual and blended treatment in the domain of depression
title Predicting therapy success for treatment as usual and blended treatment in the domain of depression
title_full Predicting therapy success for treatment as usual and blended treatment in the domain of depression
title_fullStr Predicting therapy success for treatment as usual and blended treatment in the domain of depression
title_full_unstemmed Predicting therapy success for treatment as usual and blended treatment in the domain of depression
title_short Predicting therapy success for treatment as usual and blended treatment in the domain of depression
title_sort predicting therapy success for treatment as usual and blended treatment in the domain of depression
topic Special issue for the ISRII 2017 meeting
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5945603/
https://www.ncbi.nlm.nih.gov/pubmed/29862165
http://dx.doi.org/10.1016/j.invent.2017.08.003
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