Personalized treatment selection in routine care: Integrating machine learning and statistical algorithms to recommend cognitive behavioral or psychodynamic therapy
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...
Paper Details
Title
Personalized treatment selection in routine care: Integrating machine learning and statistical algorithms to recommend cognitive behavioral or psychodynamic therapy
Published Date
May 28, 2020
Journal
Volume
31
Issue
1
Pages
33 - 51
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