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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Vydané v:	Psychotherapy research Ročník 31; číslo 1; s. 33 - 51
Hlavní autori:	Schwartz, Brian, Cohen, Zachary D., Rubel, Julian A., Zimmermann, Dirk, Wittmann, Werner W., Lutz, Wolfgang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	England Routledge 2021 Taylor & Francis Ltd
Predmet:	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
On-line prístup:	Získať plný text
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