Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Modeling in Therapy01:26

Modeling in Therapy

61
Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...
61

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Task-Preserving EEG Anonymization Using Latent Feature Masking.

IEEE journal of biomedical and health informatics·2026
Same author

Ghost poisoning: Making users invisible to speaker verification models.

JASA express letters·2026
Same authorSame journal

Multimodal assessments of therapist characteristics are largely unrelated to patient outcomes: A preregistered analysis.

Clinical psychological science : a journal of the Association for Psychological Science·2026
Same author

Temporal patterns in articulation underlying repetitions, prolongations and blocks.

Journal of fluency disorders·2026
Same author

Neural Responses to Affective Sentences Reveal Signatures of Depression.

Translational psychiatry·2026
Same author

Time-resolved EEG decoding reveals altered neural dynamics of affective semantic evaluation in depression and suicidality.

Communications biology·2026

Related Experiment Video

Updated: Jun 17, 2025

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment
05:19

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

2.2K

Machine-Learning-Based Prediction of Client Distress From Session Recordings.

Patty B Kuo1, Michael J Tanana1, Simon B Goldberg2

  • 1University of Utah.

Clinical Psychological Science : a Journal of the Association for Psychological Science
|August 6, 2024
PubMed
Summary

Natural language processing (NLP) can predict client symptom improvement using previous therapy session transcripts. This machine learning approach shows promise for large-scale outcome monitoring in mental healthcare.

Keywords:
Machine learningnatural language processingoutcome predictionpsychotherapy

More Related Videos

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.0K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.7K

Related Experiment Videos

Last Updated: Jun 17, 2025

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment
05:19

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

2.2K
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.0K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.7K

Area of Science:

  • Computational linguistics
  • Clinical psychology
  • Machine learning

Background:

  • Natural language processing (NLP) offers potential for large-scale evaluation of therapist-client interactions and client outcome feedback.
  • Limited research exists on applying NLP to client outcome prediction using direct interaction transcripts, accounting for linguistic complexity, and employing robust model development practices.

Purpose of the Study:

  • To develop and evaluate NLP models for predicting client symptom improvement directly from therapist-client interaction transcripts.
  • To address limitations in previous studies by incorporating contextual linguistic factors and rigorous training/testing protocols.

Main Methods:

  • Utilized 2,630 session recordings from 795 clients and 56 therapists.
  • Developed NLP models to predict client symptoms in a given session based on the preceding session's transcript.
  • Employed best practices for classical training and test splits in model development.

Main Results:

  • The developed NLP models successfully predicted client symptom improvement with a Spearman's rho of 0.32 (p<.001).
  • Results demonstrate the feasibility of using session transcripts as direct predictors of therapeutic outcomes.

Conclusions:

  • NLP models show significant potential for implementation in clinical outcome monitoring systems.
  • This approach can enhance the quality of mental healthcare through data-driven feedback and progress tracking.
  • Further research and applications are warranted to explore the full capabilities of NLP in psychotherapy research.