测试使用大型语言模型从治疗会话转录文本创建个性化网络的效用：一项概念验证研究

Computer Science > Artificial Intelligence arXiv:2512.05836 (cs) [Submitted on 5 Dec 2025 (v1), last revised 25 Sep 2026 (this version, v2)] Title:Testing the Utility of Using Large Language Models to Create Personalized Networks From Therapy Session Transcripts: A Proof of Concept Study Authors:Clarissa W. Ong, Hiba Arnaout, Kate Sheehan, Estella Fox, Eugen Owtscharow, Iryna Gurevych View a PDF of the paper titled Testing the Utility of Using Large Language Models to Create Personalized Networks From Therapy Session Transcripts: A Proof of Concept Study, by Clarissa W. Ong and 5 other authors View PDF Abstract:Recent advances in psychotherapy have focused on treatment personalization, such as by selecting treatment modules based on individual networks. However, estimating personalized networks typically requires intensive longitudinal data, which is not always feasible to collect. A solution to increase scalability of network-driven treatment personalization is leveraging large language models (LLMs). In this study, we developed an end-to-end pipeline for automatically generating client networks to support case conceptualization and treatment planning. We annotated 8,028 utterances from 77 therapy transcripts (N = 6). In the first stage of the pipeline, we identified clinically relevant processes (binary classification) and their corresponding dimensions (multi-label classification). Then, we introduced a two-step method that grouped the processes into clinically meaningful clusters and generated labels for the clusters. Finally, we generated connections between clusters. Evaluation results generally supported model utility, however, interrater agreement on model performance metrics was inconsistent, ranging from poor to substantial. Qualitative examination of the networks indicated consistency with original study data. Given coherence and interpretability of the generated networks, developing networks from therapy transcripts using LLMs appears to be feasible. Nonetheless, more research is needed to examine whether these networks improve treatment outcomes, including relative to other methods of treatment personalization, such as statistically estimated networks. Potential use cases and limitations of our pipeline are discussed. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2512.05836 [cs.AI] (or arXiv:2512.05836v2 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2512.05836 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Hiba Arnaout [view email] [v1] Fri, 5 Dec 2025 16:12:12 UTC (416 KB) [v2] Fri, 25 Sep 2026 15:24:54 UTC (1,797 KB) Full-text links: Access Paper: View a PDF of the paper titled Testing the Utility of Using Large Language Models to Create Personalized Networks From Therapy Session Transcripts: A Proof of Concept Study, by Clarissa W. Ong and 5 other authors View PDF view license Current browse context: cs.AI new | recent | 2025-12 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)