PatientSim：基于人物角色的医患互动真实模拟器

摘要

医患咨询需要多轮次、情境感知的交流，并针对多样化的患者角色进行定制。在此类场景中训练或评估医生大语言模型（LLMs）需要真实的患者互动系统。然而，现有的模拟器往往无法全面反映临床实践中遇到的各种患者角色。为解决这一问题，我们引入了PatientSim，这是一个基于医学专业知识，为临床场景生成真实且多样化患者角色的患者模拟器。PatientSim通过以下两种方式运作：1）临床档案，包括从MIMIC-ED和MIMIC-IV数据集中提取的真实世界数据中的症状和医疗史；2）由四个维度定义的角色：性格、语言熟练度、医疗史回忆水平及认知混淆程度，共形成37种独特组合。我们评估了八种LLMs在事实准确性和角色一致性上的表现。表现最佳的开源模型Llama 3.3，经过四位临床医生的验证，确认了我们框架的稳健性。作为一个开源、可定制的平台，PatientSim提供了一个可复制且可扩展的解决方案，能够根据特定培训需求进行定制。它提供了一个符合隐私保护的环境，作为评估医疗对话系统在不同患者表现下的强大测试平台，并展现出作为医疗教育工具的潜力。

English

Doctor-patient consultations require multi-turn, context-aware communication tailored to diverse patient personas. Training or evaluating doctor LLMs in such settings requires realistic patient interaction systems. However, existing simulators often fail to reflect the full range of personas seen in clinical practice. To address this, we introduce PatientSim, a patient simulator that generates realistic and diverse patient personas for clinical scenarios, grounded in medical expertise. PatientSim operates using: 1) clinical profiles, including symptoms and medical history, derived from real-world data in the MIMIC-ED and MIMIC-IV datasets, and 2) personas defined by four axes: personality, language proficiency, medical history recall level, and cognitive confusion level, resulting in 37 unique combinations. We evaluated eight LLMs for factual accuracy and persona consistency. The top-performing open-source model, Llama 3.3, was validated by four clinicians to confirm the robustness of our framework. As an open-source, customizable platform, PatientSim provides a reproducible and scalable solution that can be customized for specific training needs. Offering a privacy-compliant environment, it serves as a robust testbed for evaluating medical dialogue systems across diverse patient presentations and shows promise as an educational tool for healthcare.

PatientSim：基于人物角色的医患互动真实模拟器

PatientSim: A Persona-Driven Simulator for Realistic Doctor-Patient Interactions

摘要

Summary

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