用大模型生成真实可信的抑郁症患者对话，助力诊断系统训练
TalkDep: Clinically Grounded LLM Personas for Conversation-Centric Depression Screening
- 基于临床诊断标准构建医生参与的模拟患者生成流程
- 生成的对话通过专业医生评估，具备真实症状多样性
- 适合用于提升抑郁症自动诊断系统的泛化能力
心理健康服务需求激增，但临床训练数据稀缺，制约了抑郁诊断能力发展。现有虚拟患者模拟方法常无法生成符合临床规范、自然且多样的症状表现。本文提出新型临床介入式患者模拟框架TalkDep，利用先进语言模型，结合精神科诊断标准、症状严重程度量表和上下文因素进行条件控制，生成真实可信的患者对话。通过临床专家的多维度评估验证其可靠性。该可验证的模拟患者资源具备可扩展性与适应性，能有效提升自动抑郁诊断系统的鲁棒性与泛化能力。
原文摘要 · Abstract (English)
The increasing demand for mental health services has outpaced the availability of real training data to develop clinical professionals, leading to limited support for the diagnosis of depression. This shortage has motivated the development of simulated or virtual patients to assist in training and evaluation, but existing approaches often fail to generate clinically valid, natural, and diverse symptom presentations. In this work, we embrace the recent advanced language models as the backbone and propose a novel clinician-in-the-loop patient simulation pipeline, TalkDep, with access to diversified patient profiles to develop simulated patients. By conditioning the model on psychiatric diagnostic criteria, symptom severity scales, and contextual factors, our goal is to create authentic patient responses that can better support diagnostic model training and evaluation. We verify the reliability of these simulated patients with thorough assessments conducted by clinical professionals. The availability of validated simulated patients offers a scalable and adaptable resource for improving the robustness and generalisability of automatic depression diagnosis systems.
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