合成还是真实？从纵向证据构建心理健康患者模拟器
Synthetic or Authentic? Building Mental Patient Simulators from Longitudinal Evidence
AI总结：
本文提出DEPROFILE框架，通过整合多源数据构建统一患者档案，提升对话真实性和行为多样性。
摘要：
患者模拟对于开发和评估心理健康对话系统至关重要。由于大多数现有方法依赖快照式提示且信息有限，多轮交互中同质化行为和不连贯的疾病进程成为关键挑战。本文提出DEPROFILE，一种基于数据的患者模拟框架，通过整合人口属性、标准化临床症状、咨询对话和纵向生活事件历史构建统一的多源患者档案。我们进一步引入Chain-of-Change代理，将噪声的纵向记录转化为结构化、时间感知的记忆表示用于模拟。在多个大型语言模型（LLM）基础上的实验表明，通过DEPROFILE构建的更全面的档案，对话真实性、行为多样性和事件丰富性持续提升并超过现有最佳基线，突显了在可验证的纵向证据基础上构建患者模拟的重要性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
