Personalized treatment selection in routine care: Integrating machine learning and statistical algorithms to recommend cognitive behavioral or psychodynamic therapy

Objective: This study aims at developing a treatment selection algorithm using a combination of machine learning and statistical inference to recommend patients' optimal treatment based on their pre-treatment characteristics. Methods: A disorder-heterogeneous, naturalistic sample of N = 1,379 o...

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Published in:Psychotherapy research Vol. 31; no. 1; pp. 33 - 51
Main Authors: Schwartz, Brian, Cohen, Zachary D., Rubel, Julian A., Zimmermann, Dirk, Wittmann, Werner W., Lutz, Wolfgang
Format: Journal Article
Language:English
Published: England Routledge 2021
Taylor & Francis Ltd
Subjects:
Algorithms
Averages
Clinical decision making
Cognition
Cognitive behavioral therapy
Cognitive-behavioral factors
Data
Decision making
Inference
Machine learning
Medical decision making
Medical treatment
outcome prediction
outpatient psychotherapy
Outpatients
Patients
precision medicine
Psychodynamic therapy
Psychotherapy
random forest
Statistical inference
Statistics
Therapists
Training
variable selection
outcome prediction
outpatient psychotherapy
precision medicine
random forest
variable selection
machine learning
ISSN:1050-3307, 1468-4381, 1468-4381
Online Access:Get full text
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Summary:Objective: This study aims at developing a treatment selection algorithm using a combination of machine learning and statistical inference to recommend patients' optimal treatment based on their pre-treatment characteristics. Methods: A disorder-heterogeneous, naturalistic sample of N = 1,379 outpatients treated with either cognitive behavioral therapy or psychodynamic therapy was analyzed. Based on a combination of random forest and linear regression, differential treatment response was modeled in the training data (n = 966) to indicate each individual's optimal treatment. A separate holdout dataset (n = 413) was used to evaluate personalized recommendations. Results: The difference in outcomes between patients treated with their optimal vs. non-optimal treatment was significant in the training data, but non-significant in the holdout data (b = -0.043, p = .280). However, for the 50% of patients with the largest predicted benefit of receiving their optimal treatment, the average percentage of change on the BSI in the holdout data was 52.6% for their optimal and 38.4% for their non-optimal treatment (p = .017; d = 0.33 [0.06, 0.61]). Conclusion: A treatment selection algorithm based on a combination of ML and statistical inference might improve treatment outcome for some, but not all outpatients and could support therapists' clinical decision-making.
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ISSN:1050-3307
1468-4381
1468-4381
DOI:10.1080/10503307.2020.1769219