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Cited by 25Psychotherapy Techniques and ApplicationsArtificial Intelligence in HealthcareAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes

What the future holds: Machine learning to predict success in psychotherapy

Taubitz, Friedrich-Samuel, Büdenbender, Björn, Alpers, Georg W.·2022·Behaviour Research and Therapy
DOI 10.1016/j.brat.2022.104116
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Abstract

Machine learning (ML) may help to predict successful psychotherapy outcomes and to identify relevant predictors of success. So far, ML applications are scant in psychotherapy research and they are typically based on small samples or focused on specific diagnoses. In this study, we predict successful therapy outcomes with ML in a heterogeneous sample in routine outpatient care. We trained established ML models (decision trees and ensembles of them) with routinely collected clinical baseline information from n = 685 outpatients to predict a successful outcome of cognitive behavioral therapy. Treatment success was defined as clinically significant change (CSC) on the Brief-Symptom-Checklist (reached by 326 patients; 48%). The best performing model (Gradient Boosting Machines) achieved a balanced accuracy of 69% (p < .001) on unseen validation data. Out of 383 variables, we identified the 16 most important predictors, which were still able to predict CSC with 67% balanced accuracy. Our study demonstrates that ML models built on data, which is typically available at the outset of therapy, can predict whether an individual will substantially benefit from the intervention. Some of the predictors were theoretically expected (e.g., level of functioning), but others need further validation (e.g., somatization). From a theoretical and practical perspective, ML is clearly an attractive addition to more established psychotherapy research methodology.

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  • Traditionally, the combination of AI and psychotherapy has been used to retrospectively evaluate large amounts of data once a psychological intervention has finished, to evaluate outcome data that can predict a better response to a given psychological treatment or to identify risks for becoming a not-on-track patient once the treatment ended.

    “Traditionally, the combination of AI and psychotherapy has been used to retrospectively evaluate large amounts of data once a psychological intervention has finished (i.e., to evaluate the outcome data that can predict a better response to a given psychological treatment or to identify risks for becoming a not-on-track patient once the treatment ended) [87].”

    From Using Artificial Intelligence to Enhance Ongoing Psychological Interventions for Emotional Problems in Real- or Close to Real-Time: A Systematic Review (2022) · cites this paper as [87]

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Authors
Taubitz, F.-S., Büdenbender, B., & Alpers, G. W.
Affiliations
University of Mannheim (DE)
Corresponding author
Alpers, Georg W.
ORCID
Venue
Behaviour Research and Therapy
Publisher
Elsevier BV
Published
2022-09
Volume
156
Pages
104116
ISSN
0005-7967 (ISSN-L)
DOI
10.1016/j.brat.2022.104116
OpenAlex
W4280525119
Cited by
25
References
89
Citations by year
2026: 6 · 2025: 6 · 2024: 7 · 2023: 4 · 2022: 2
FWCI
4.33
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Behaviour Research and Therapy (publishedVersion)
  • PubMed (publishedVersion)
  • MADOC (University of Mannheim) (submittedVersion)
Topics
  • Social Sciences › Psychology › Clinical Psychology › Psychotherapy Techniques and Applications
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Keywords
psychotherapy outcome prediction, cognitive behavioral therapy, treatment response, gradient boosting, decision tree, somatization, predictors of treatment success, outpatient psychotherapy
MeSH terms
Machine Learning, Humans, Outpatients, Psychotherapy, Cognitive Behavioral Therapy, Treatment Outcome

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