用电子病历生成真实感多角色急救对话，提升诊断预测效果
EMSDialog: Synthetic Multi-person Emergency Medical Service Dialogue Generation from Electronic Patient Care Reports via Multi-LLM Agents
- 多智能体协作生成对话，基于电子病历和话题流迭代优化
- 构建4414条合成对话数据，包含43种诊断与逐轮话题标注
- 适合急救对话建模、医疗AI训练，提升诊断准确与时效
对话式诊断预测需在持续的临床对话中追踪动态证据，并判断何时做出诊断。现有医疗对话数据集多为双人对话，缺乏多方协作流程及相应标注。本文提出一种基于电子患者护理报告（ePCR）和话题流的多智能体生成管道，通过规则化事实与话题连贯性检查，迭代规划、生成并自洽修正对话。该方法生成了4,414条合成的多说话人急救服务对话数据集（EMSDialog），涵盖43种诊断、说话人角色及每轮话题标注。人工与大模型评估表明，该数据集在语句与对话层面均具备高真实性和质量。实验显示，使用该数据集增强训练可显著提升急救对话诊断预测的准确性、及时性与稳定性。数据集与代码已公开。
原文摘要 · Abstract (English)
Conversational diagnosis prediction requires models to track evolving evidence in streaming clinical conversations and decide when to commit to a diagnosis. Existing medical dialogue corpora are largely dyadic or lack the multi-party workflow and annotations needed for this setting. We introduce an ePCR-grounded, topic-flow-based multi-agent generation pipeline that iteratively plans, generates, and self-refines dialogues with rule-based factual and topic flow checks. The pipeline yields EMSDialog, a dataset of 4,414 synthetic multi-speaker EMS conversations based on a real-world ePCR dataset, annotated with 43 diagnoses, speaker roles, and turn-level topics. Human and LLM evaluations confirm high quality and realism of EMSDialog using both utterance- and conversation-level metrics. Results show that EMSDialog-augmented training improves accuracy, timeliness, and stability of EMS conversational diagnosis prediction. Our datasets and code are publicly available at https://uva-dsa.github.io/EMSDialog
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