Testing the Utility of Using Large Language Models to Create Personalized Networks From Therapy Session Transcripts: A Proof of Concept Study

The study presents a pipeline that uses large language models to automatically generate personalized client networks from therapy session transcripts, aiming to aid case conceptualization and treatment planning. Researchers annotated 8,028 utterances from 77 transcripts, then used binary and multi-label classification to identify clinically relevant processes and dimensions, clustered these processes, and generated connections between clusters. Evaluation showed general support for the model’s utility, though interrater agreement varied, and qualitative analysis indicated the networks were coherent and interpretable, suggesting feasibility while highlighting the need for further research on outcome improvement.