用模拟医患对话训练自进化精神科医生，提升抑郁诊断准确率
Depression Diagnosis Dialogue Simulation: Self-improving Psychiatrist with Tertiary Memory
- 构建三级记忆的精神科对话代理，自我优化诊断流程
- 在真实场景数据集上实现高精度抑郁与自杀风险识别
- 无需微调大模型权重，少量标注数据即可有效部署
抑郁症等心理健康问题在当代社会面临严峻挑战，亟需高效的自动化诊断方法。本文提出代理精神科诊所（Agent Mental Clinic, AMC），一个通过患者与精神科医生代理模拟对话来提升抑郁诊断能力的自进化对话系统。为提高对话质量与诊断准确性，设计了具备三级记忆结构的精神科代理，包含对话控制与反思插件作为“监督者”以及记忆采样模块，充分调动代理的专业能力，在真实场景收集的数据集上实现了对话形式下的抑郁风险与自杀风险诊断高精度。实验表明，该系统模拟精神科医生培训过程，可在不修改大语言模型权重的前提下，有效对齐特定领域的真实分布，即使仅有少量代表性标注案例亦可实现良好性能。
原文摘要 · Abstract (English)
Mental health issues, particularly depressive disorders, present significant challenges in contemporary society, necessitating the development of effective automated diagnostic methods. This paper introduces the Agent Mental Clinic (AMC), a self-improving conversational agent system designed to enhance depression diagnosis through simulated dialogues between patient and psychiatrist agents. To enhance the dialogue quality and diagnosis accuracy, we design a psychiatrist agent consisting of a tertiary memory structure, a dialogue control and reflect plugin that acts as ``supervisor'' and a memory sampling module, fully leveraging the skills reflected by the psychiatrist agent, achieving great accuracy on depression risk and suicide risk diagnosis via conversation. Experiment results on datasets collected in real-life scenarios demonstrate that the system, simulating the procedure of training psychiatrists, can be a promising optimization method for aligning LLMs with real-life distribution in specific domains without modifying the weights of LLMs, even when only a few representative labeled cases are available.
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