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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Veröffentlicht in:	Psychotherapy research Jg. 31; H. 1; S. 33 - 51
Hauptverfasser:	Schwartz, Brian, Cohen, Zachary D., Rubel, Julian A., Zimmermann, Dirk, Wittmann, Werner W., Lutz, Wolfgang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	England Routledge 2021 Taylor & Francis Ltd
Schlagworte:	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-Zugang:	Volltext
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