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Measuring Alliance and Symptom Severity in Psychotherapy Transcripts Using Bert Topic Modeling.

Christopher Lalk¹, Tobias Steinbrenner², Weronika Kania²

¹Department of Psychology, Osnabrück University, Osnabrück, Germany. Christopher.lalk@uni-osnabrueck.de.

Administration and Policy in Mental Health

|March 29, 2024

Summary

Topic modeling accurately predicts psychotherapy outcomes from patient and therapist transcripts. Patient speech topics predict symptom severity, while therapist speech topics predict therapeutic alliance, revealing key themes for treatment improvement.

Keywords:

Computational psychotherapy research Explainable artificial intelligence Machine learning Natural language processing

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Area of Science:

Psychology
Computational Linguistics
Artificial Intelligence

Background:

Psychotherapy research increasingly utilizes computational methods to analyze session transcripts.
Topic modeling offers a novel approach to uncover latent themes and predict clinical outcomes.

Purpose of the Study:

To apply topic modeling to psychotherapy transcripts for predicting symptom severity and therapeutic alliance.
To identify key topics and themes driving these predictions using explainable AI (XAI).

Main Methods:

Analysis of 552 psychotherapy transcripts from 124 patients using BERTopic for topic extraction.
Machine learning models employed to predict symptom severity and alliance from extracted patient and therapist topics.
Qualitative analysis and XAI used to identify and interpret significant topics and themes.

Main Results:

Patient topics significantly predicted symptom severity (R²=0.45), with themes of health and negative experiences being key drivers.
Therapist topics significantly predicted therapeutic alliance (R²=0.20), with themes related to the psychotherapy framework, income, and daily life being associated with lower alliance.
Sensitivity analyses confirmed the robustness of the findings across different modeling approaches.

Conclusions:

Topic modeling demonstrates significant potential for predicting treatment-relevant metrics in psychotherapy research.
XAI enhances interpretability by detailing the predictive value of specific topics and themes.
Findings highlight the distinct contributions of patient and therapist discourse to treatment outcomes.