Machine learning and natural language processing in psychotherapy research: Alliance as example use case.

TLDR

Artificial intelligence and machine learning are increasingly integrated into modern life and scientific research, offering promise for addressing limitations in mental health care and psychotherapy, and raising questions about dissemination and implementation. The study aims to introduce machine learning and natural language processing for automating assessment of psychotherapy processes, and offers six practical suggestions for conducting such research along with future directions. The authors processed 1,235 therapy session recordings from 386 clients and 40 therapists using automatic speech recognition, then trained machine learning algorithms to learn associations between linguistic content and client‑rated therapeutic alliance. The models modestly predicted alliance ratings from session content (Spearman's ρ = .15, p < .001), demonstrating the potential of NLP and machine learning to predict a key psychotherapy process variable. © 2020 APA, all rights reserved.

Abstract

Artificial intelligence generally and machine learning specifically have become deeply woven into the lives and technologies of modern life. Machine learning is dramatically changing scientific research and industry and may also hold promise for addressing limitations encountered in mental health care and psychotherapy. The current paper introduces machine learning and natural language processing as related methodologies that may prove valuable for automating the assessment of meaningful aspects of treatment. Prediction of therapeutic alliance from session recordings is used as a case in point. Recordings from 1,235 sessions of 386 clients seen by 40 therapists at a university counseling center were processed using automatic speech recognition software. Machine learning algorithms learned associations between client ratings of therapeutic alliance exclusively from session linguistic content. Using a portion of the data to train the model, machine learning algorithms modestly predicted alliance ratings from session content in an independent test set (Spearman's ρ = .15, p < .001). These results highlight the potential to harness natural language processing and machine learning to predict a key psychotherapy process variable that is relatively distal from linguistic content. Six practical suggestions for conducting psychotherapy research using machine learning are presented along with several directions for future research. Questions of dissemination and implementation may be particularly important to explore as machine learning improves in its ability to automate assessment of psychotherapy process and outcome. (PsycInfo Database Record (c) 2020 APA, all rights reserved).