用结构化病历生成急诊分诊对话，模拟护士与患者互动。
TriageSim: A Conversational Emergency Triage Simulation Framework from Structured Electronic Health Records
- 基于电子病历生成带人物设定的多轮分诊对话
- 产出约800条合成对话文本及对应音频
- 适合研究急诊对话系统或医疗人工智能的开发者
由于对护患互动的监管限制，急诊分诊研究受限于结构化电子健康记录（EHR）。我们提出TriageSim，一个从结构化记录生成角色化分诊对话的仿真框架。该框架支持多轮护患交互，并可显式控制话语不流畅性和决策行为，生成约800条合成对话文本及对应音频。通过自动化分析语言、行为和声学保真度，以及对随机抽取的50条对话进行人工评估以检验医学保真度，验证了数据质量。利用生成语料进行对话分诊分类任务，结果显示在三种模态（合成文本、语音识别转录、原始音频）间对分诊级别存在适度一致性。代码已开源：https://github.com/dipankarsrirag/triage-sim.git。
原文摘要 · Abstract (English)
Research in emergency triage is restricted to structured electronic health records (EHR) due to regulatory constraints on nurse-patient interactions. We introduce TriageSim, a simulation framework for generating persona-conditioned triage conversations from structured records. TriageSim enables multi-turn nurse-patient interactions with explicit control over disfluency and decision behaviour, producing a corpus of ~800 synthetic transcripts and corresponding audio. We use a combination of automated analysis for linguistic, behavioural and acoustic fidelity alongside manual evaluation for medical fidelity using a random subset of 50 conversations. The utility of the generated corpus is examined via conversational triage classification. We observe modest agreement for acuity levels across three modalities: generated synthetic text, ASR transcripts, and direct audio inputs. We provide the code for TriageSim at https://github.com/dipankarsrirag/triage-sim.git.
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