TRUST：一个基于LLM的创伤理解与结构化评估对话系统
TRUST: An LLM-Based Dialogue System for Trauma Understanding and Structured Assessments
AI总结：
本研究开发了基于LLM的对话系统框架TRUST，用于PTSD诊断访谈与评估，通过设计Dialogue Acts schema和患者模拟方法，专家评估显示其表现已达到普通临床医生水平。
摘要：
目标：尽管大型语言模型（LLMs）已被广泛用于辅助临床医生和支持患者，但目前尚无现有工作探索用于标准诊断访谈和评估的对话系统。本研究旨在通过开发一个能复刻临床医生行为的LLM驱动的对话系统，来弥合心理健康护理可及性的差距。材料与方法：我们引入了TRUST，这是一个由协作式LLM模块组成的框架，能够针对创伤后应激障碍（PTSD）进行正式的诊断访谈和评估。为了指导生成恰当的临床回复，我们提出了一种专为临床访谈设计的Dialogue Acts schema。此外，我们开发了一种基于真实访谈记录的患者模拟方法，以替代临床医生耗时且昂贵的手动测试。结果：设计了一套全面的评估指标，从智能体和患者模拟两个视角评估该对话系统。由对话和临床专家进行的专家评估表明，TRUST的表现与现实临床访谈相当。讨论：我们的系统达到了普通临床医生的水平，在沟通风格和回复恰当性方面仍有未来提升的空间。结论：我们的TRUST框架展示了其促进心理健康护理普及的潜力。
英文摘要：
Objectives: While Large Language Models (LLMs) have been widely used to assist clinicians and support patients, no existing work has explored dialogue systems for standard diagnostic interviews and assessments. This study aims to bridge the gap in mental healthcare accessibility by developing an LLM-powered dialogue system that replicates clinician behavior. Materials and Methods: We introduce TRUST, a framework of cooperative LLM modules capable of conducting formal diagnostic interviews and assessments for Post-Traumatic Stress Disorder (PTSD). To guide the generation of appropriate clinical responses, we propose a Dialogue Acts schema specifically designed for clinical interviews. Additionally, we develop a patient simulation approach based on real-life interview transcripts to replace time-consuming and costly manual testing by clinicians. Results: A comprehensive set of evaluation metrics is designed to assess the dialogue system from both the agent and patient simulation perspectives. Expert evaluations by conversation and clinical specialists show that TRUST performs comparably to real-life clinical interviews. Discussion: Our system performs at the level of average clinicians, with room for future enhancements in communication styles and response appropriateness. Conclusions: Our TRUST framework shows its potential to facilitate mental healthcare availability.
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