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Prediction of outcome in internet-delivered cognitive behaviour therapy for paediatric obsessive-compulsive disorder: A machine learning approach
Stockholm University, Faculty of Social Sciences, Department of Psychology, Clinical psychology. Karolinska Institutet, Sweden.
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2018 (English)In: International Journal of Methods in Psychiatric Research, ISSN 1049-8931, E-ISSN 1557-0657, Vol. 27, no 1, article id e1576Article in journal (Refereed) Published
Abstract [en]

Background: There are no consistent predictors of treatment outcome in paediatric obsessive–compulsive disorder (OCD). One reason for this might be the use of suboptimal statistical methodology. Machine learning is an approach to efficiently analyse complex data. Machine learning has been widely used within other fields, but has rarely been tested in the prediction of paediatric mental health treatment outcomes.

Objective: To test four different machine learning methods in the prediction of treatment response in a sample of paediatric OCD patients who had received Internet-delivered cognitive behaviour therapy (ICBT).

Methods: Participants were 61 adolescents (12–17 years) who enrolled in a randomized controlled trial and received ICBT. All clinical baseline variables were used to predict strictly defined treatment response status three months after ICBT. Four machine learning algorithms were implemented. For comparison, we also employed a traditional logistic regression approach.

Results: Multivariate logistic regression could not detect any significant predictors. In contrast, all four machine learning algorithms performed well in the prediction of treatment response, with 75 to 83% accuracy.

Conclusions: The results suggest that machine learning algorithms can successfully be applied to predict paediatric OCD treatment outcome. Validation studies and studies in other disorders are warranted.

Place, publisher, year, edition, pages
2018. Vol. 27, no 1, article id e1576
Keyword [en]
paediatric obsessive-compulsive disorder, internet-delivered, CBT
National Category
Psychology Psychiatry
Research subject
Psychology
Identifiers
URN: urn:nbn:se:su:diva-154151DOI: 10.1002/mpr.1576ISI: 000426505900005OAI: oai:DiVA.org:su-154151DiVA, id: diva2:1191253
Note

This study was funded by Stockholm County Council (PPG project 20120167, 20140085), the Swedish Research Council for Health, Working Life and Welfare (2014-4052) and the Jane & Dan Olsson Foundation. Dr Rück was supported by a grant from the Swedish Research Council (K2013-61P-22168).

Available from: 2018-03-16 Created: 2018-03-16 Last updated: 2018-04-03Bibliographically approved

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