让AI病人情绪随对话变化，提升临终关怀沟通训练真实感
EmoPatient: An Emotion-Directed Patient Simulator for Realistic Palliative Care Communication Training
- 用情感导演代理动态调控病人情绪强度与互动方向
- 在4项情绪真实度指标上优于基线模型，适配不同人格类型
- 适合医学教育者和临床沟通培训人员使用
临终关怀沟通中的有效交流是关键临床技能，但训练医护人员应对复杂患者情绪仍具挑战。基于大语言模型的患者模拟器虽具可扩展性，但多数系统将患者情绪视为静态，未能捕捉临床互动中动态的情绪变化。我们提出EmoPatient，一种面向情绪演化的临终关怀患者模拟器。该系统引入情感导演代理，实时估算患者情绪状态，并生成每轮对话的情感强度、调节稳定性和交互引导控制信号。通过多轮医生-患者对话模拟实验，对比基线模拟器，结果表明在四项理论驱动的情绪真实度指标上均有提升，且在不同人格变体下表现稳健，说明建模情绪动态可显著增强大语言模型患者模拟器在临终关怀沟通训练中的真实性。
原文摘要 · Abstract (English)
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.
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