Using topic models to identify clients’ functioning levels and alliance ruptures in psychotherapy.

Abstract

Computerized natural language processing techniques can analyze psychotherapy sessions as texts, thus generating information about the therapy process and outcome and supporting the scaling-up of psychotherapy research. We used topic modeling to identify topics discussed in psychotherapy sessions and explored (a) which topics best identified clients' functioning and alliance ruptures and (b) whether changes in these topics were associated with changes in outcome. Transcripts of 873 sessions from 58 clients treated by 52 therapists were analyzed. Before each session, clients self-reported functioning and symptom level. After each session, therapists reported the extent of alliance rupture. Latent Dirichlet allocation was used to extract latent topics from psychotherapy textual data. Then a sparse multinomial logistic regression model was used to predict which topics best identified clients' functioning levels and the occurrence of alliance ruptures in psychotherapy sessions. Finally, we used multilevel growth models to explore the associations between changes in topics and changes in outcome. Session-based processing yielded a list of semantic topics. The model identified the labels above chance (65% to 75% accuracy). Change trajectories in topics were associated with change trajectories in outcome. The results suggest that topic models can exploit rich linguistic data within sessions to identify psychotherapy process and outcomes. Clinical Impact StatementQuestion: Can machine learning techniques identify the topics discussed in psychotherapy sessions and examine the associations between these topics and treatment process and outcome? Findings: Topic modeling yielded semantically meaningful topics that were then used to identify which topics were most closely associated with the level of clients' functioning and those that were most closely associated with rupture occurrence. Changes in these topics were associated with changes in outcome. Meaning: Topic modeling can enable therapists to be better attuned to specific topics that may signal important events in therapy. Using topic model output, therapists can access a summary of topics discussed in a session, locate specific themes associated with rupture or with clients' deterioration, and orient interventions to improve the situation. Next Steps: Future studies could use topic model output alongside existing monitoring tools to inform therapists of meaningful linguistic processes that occur within psychotherapy sessions.

Text Topics and Treatment Response in Internet-Delivered Cognitive Behavioral Therapy for Generalized Anxiety Disorder: Text Mining Study

“…Our topic models successfully extracted meaningful topics from iCBT texts, some of which were associated with treatment response in an interpretable manner. This is in line with previous psychotherapy topic modeling research [13,18]. Our results extend the previous work by demonstrating topic modeling to be suitable for iCBT task-specific data.…”

“…Thus, our data likely constitute a representative sample from the target patient population. Our data set was also fairly large in terms of individual patients, improving the generalizability of our results when compared with previous studies using topic modeling to predict outcomes in samples of under 100 patients [13,18]. Our text corpus used in topic modeling consisted of entries on a task sheet targeted for writing about worries.…”

“…Topic models, such as the latent Dirichlet allocation (LDA), are unsupervised machine learning models that reduce data dimensionality by expressing a text as a mixture of latent topics [11]. LDA has been successful in detecting meaningful topics that occur in face-to-face psychotherapy transcripts [12][13][14]. A recent study that applied LDA to psychotherapy session transcripts found a covariation between topics with descriptions of positive experiences and a symptom decrease whereas topics that reflected discussion about treatment were associated with a symptom increase [13].…”

“…LDA has been successful in detecting meaningful topics that occur in face-to-face psychotherapy transcripts [12][13][14]. A recent study that applied LDA to psychotherapy session transcripts found a covariation between topics with descriptions of positive experiences and a symptom decrease whereas topics that reflected discussion about treatment were associated with a symptom increase [13]. The latter also predicted alliance rupture in therapy sessions.…”

Hebrew Psychological Lexicons

“…The results suggest that topic models can exploit rich linguistic data within sessions to identify psychotherapy process and outcomes. For the detailed study see Atzil-Slonim et al (2021).…”