用真实长期病历构建更逼真的精神疾病模拟患者
Synthetic or Authentic? Building Mental Patient Simulators from Longitudinal Evidence
- 整合多源真实数据构建统一患者档案
- 对话真实度、行为多样性显著提升，超越现有基线
- 适合心理对话系统研发与评估人员使用
患者模拟对开发和评估心理健康对话系统至关重要。现有方法多依赖有限信息的快照式提示，导致多轮交互中行为同质、病程不连贯。本文提出DEPROFILE框架，通过整合真实世界数据中的人口统计特征、标准化临床症状、咨询对话及纵向生活事件记录，构建统一的多源患者档案。进一步引入链式变化代理（Chain-of-Change agent），将嘈杂的纵向记录转化为结构化、时间对齐的记忆表征。在多个大语言模型基线上实验表明，基于DEPROFILE构建的完整档案使对话真实性、行为多样性和事件丰富性均持续提升，显著优于当前最优基线，凸显了以可验证的纵向证据为基础进行患者模拟的重要性。
原文摘要 · Abstract (English)
Patient simulation is essential for developing and evaluating mental health dialogue systems. As most existing approaches rely on snapshot-style prompts with limited profile information, homogeneous behaviors and incoherent disease progression in multi-turn interactions have become key chellenges. In this work, we propose DEPROFILE, a data-grounded patient simulation framework that constructs unified, multi-source patient profiles by integrating demographic attributes, standardized clinical symptoms, counseling dialogues, and longitudinal life-event histories from real-world data. We further introduce a Chain-of-Change agent to transform noisy longitudinal records into structured, temporally grounded memory representations for simulation. Experiments across multiple large language model (LLM) backbones show that with more comprehensive profile constructed by DEPROFILE, the dialogue realism, behavioral diversity, and event richness have consistently improved and exceed state-of-the-art baselines, highlighting the importance of grounding patient simulation in verifiable longitudinal evidence.
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