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Cited by 141Psychotherapy Techniques and ApplicationsMental Health via WritingComputational and Text Analysis Methods

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

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Goldberg, Simon B., Flemotomos, Nikolaos, Martinez, Victor R., Tanana, Michael J., Kuo, Patty B., Pace, Brian T., Villatte, Jennifer L., Georgiou, Panayiotis G., Van Epps, Jake, Imel, Zac E., Narayanan, Shrikanth S., Atkins, David C.·2020·Journal of Counseling Psychology
DOI 10.1037/cou0000382PMC PMC7393999
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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).

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Research works printed but not linked: 11

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Authors
Goldberg, S. B., Flemotomos, N., Martinez, V. R., Tanana, M. J., Kuo, P. B., Pace, B. T., Villatte, J. L., Georgiou, P. G., Van Epps, J., Imel, Z. E., Narayanan, S. S., & Atkins, D. C.
Affiliations
  • University of Wisconsin–Madison (US)
  • University of Southern California (US)
  • University of Utah (US)
  • University of Washington (US)
ORCID
Venue
Journal of Counseling Psychology
Publisher
American Psychological Association (APA)
Published
2020-07
Volume
67
Issue
4
Pages
438-448
ISSN
  • 0022-0167 (ISSN-L)
  • 1939-2168
DOI
10.1037/cou0000382
PMC
PMC7393999
OpenAlex
W3040438665
Cited by
141
References
72
Citations by year
2026: 24 · 2025: 30 · 2024: 24 · 2023: 23 · 2022: 15 · 2021: 19 · 2020: 5
FWCI
6.19
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Counseling Psychology (publishedVersion, other-oa)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • National Institutes of Health (R01/ AA018673)
  • University of Wisconsin-Madison
Topics
  • Social Sciences › Psychology › Clinical Psychology › Psychotherapy Techniques and Applications
  • Social Sciences › Psychology › Social Psychology › Mental Health via Writing
  • Social Sciences › General Social Sciences › Computational and Text Analysis Methods
Keywords
machine learning, natural language processing, therapeutic relationship, therapeutic process, session recording, speech recognition, psychotherapy outcome assessment, automated assessment, counseling center clients
MeSH terms
Machine Learning, Therapeutic Alliance, Adolescent, Adult, Mental Disorders, Counseling, Female, Humans, Male, Natural Language Processing, Professional-Patient Relations, Psychotherapy, Psychotherapeutic Processes, Universities, Biomedical Research, Young Adult

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