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
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Abstract	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.
AbstractList	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. 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. A disorder-heterogeneous, naturalistic sample of = 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 ( = 966) to indicate each individual's optimal treatment. A separate holdout dataset ( = 413) was used to evaluate personalized recommendations. 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 ( = -0.043, = .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 ( = .017; = 0.33 [0.06, 0.61]). 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. 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.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.
Author	Cohen, Zachary D. Schwartz, Brian Zimmermann, Dirk Rubel, Julian A. Lutz, Wolfgang Wittmann, Werner W.
Author_xml	– sequence: 1 givenname: Brian orcidid: 0000-0003-4695-4953 surname: Schwartz fullname: Schwartz, Brian email: schwartzb@uni-trier.de organization: University of Trier – sequence: 2 givenname: Zachary D. orcidid: 0000-0002-4883-1028 surname: Cohen fullname: Cohen, Zachary D. organization: University of California – sequence: 3 givenname: Julian A. orcidid: 0000-0002-9625-6611 surname: Rubel fullname: Rubel, Julian A. organization: Justus-Liebig-University Giessen – sequence: 4 givenname: Dirk orcidid: 0000-0002-8764-6343 surname: Zimmermann fullname: Zimmermann, Dirk organization: University of Trier – sequence: 5 givenname: Werner W. surname: Wittmann fullname: Wittmann, Werner W. organization: University of Mannheim – sequence: 6 givenname: Wolfgang orcidid: 0000-0002-5141-3847 surname: Lutz fullname: Lutz, Wolfgang organization: University of Trier
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Snippet	Objective: This study aims at developing a treatment selection algorithm using a combination of machine learning and statistical inference to recommend... This study aims at developing a treatment selection algorithm using a combination of machine learning and statistical inference to recommend patients' optimal...
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SubjectTerms	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
Title	Personalized treatment selection in routine care: Integrating machine learning and statistical algorithms to recommend cognitive behavioral or psychodynamic therapy
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