EmoPatient：用于姑息治疗沟通训练的情绪导向患者模拟器

中文摘要 AI 辅助

在姑息治疗讨论期间进行有效沟通是一项关键的临床技能，但培训临床医生管理复杂的患者情绪仍然具有挑战性。基于大语言模型（LLM）的患者模拟器为沟通训练提供了一种可扩展的方法，但大多数现有系统将患者情绪视为静态的，无法捕捉临床互动中观察到的动态情绪变化。我们提出了EmoPatient，这是一种情绪导向的患者模拟器，旨在在姑息治疗讨论期间生成不断变化的情绪反应。该系统引入了一个情绪指导代理（Emotion Director agent），用于估计患者的情绪状态，并生成轮次级的控制信号，涵盖情绪强度、调节稳定性和互动指导。我们通过受控的多轮医患对话模拟对EmoPatient进行评估，并将其与基线模拟器进行比较。结果显示，在四个基于理论的情绪真实性指标上均有提升，且在不同对话人格变体中表现出稳健性，这表明建模情绪动态可以提高基于LLM的患者模拟器在姑息治疗沟通训练中的真实性。

英文摘要

Effective communication during palliative care discussions is a critical clinical skill, yet training clinicians to manage complex patient emotions remains challenging. Large language model (LLM)-based patient simulators provide a scalable approach for communication training, but most existing systems treat patient emotion as static and fail to capture the dynamic emotional shifts observed in clinical interactions. We present EmoPatient, an emotion-directed patient simulator designed to generate evolving emotional responses during palliative care discussions. The system introduces an Emotion Director agent that estimates the patient's emotional state and generates turn-level control signals for emotional intensity, regulatory stability, and interactional guidance. We evaluate EmoPatient through controlled multi-turn physician-patient dialogue simulations and compare it with baseline simulators. Results show improvements across four theory-informed emotional realism metrics and robustness across conversational personality variants, suggesting that modeling emotional dynamics can improve the realism of LLM-based patient simulators for palliative care communication training.

发表机构

• UT Austin（德克萨斯大学奥斯汀分校）
• UT MD Anderson（德克萨斯大学MD安德森癌症中心）

机构由 AI 辅助整理，请以论文原文为准。