arXiv (All)
open access
Testing the Utility of Using Large Language Models to Create Personalized Networks From Therapy Session Transcripts: A Proof of Concept Study
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 s
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Testing the Utility of Using Large Language Models to Create Personalized Networks From Therapy Session Transcripts: A Proof of Concept Study#372250
- Interpretable Symptom Vectors for Depression in a Large Language Model#822255
- Scalable, context-sensitive psychiatric assessment with large language models and brief diaries#223244
- MACBT: A Multi-Agent Cognitive Behavioral Therapy Decision Support System with Longitudinal Memory#1105498
- Online Relapse Prevention Therapy for Patients With Alcohol Use Disorder#1101320
Record · ID 1105941
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive