用知识图谱模拟医生问诊，提升对话式诊断的准确与效率
Think Like a Doctor: Conversational Diagnosis through the Exploration of Diagnostic Knowledge Graphs
- 基于诊断知识图谱分两步推理：生成假设并提问验证
- 在MIMIC-IV数据上诊断准确率超越基线，问诊效率更高
- 真实患者模拟器支持模糊症状表达，适合临床场景测试
对话式诊断需多轮问诊，在信息不全时逐步缩小鉴别诊断范围。现有方法依赖模型参数化知识或假设患者提供详尽信息，不切实际。为此，我们提出一种基于诊断知识图谱的对话诊断系统，分两步推理：(i) 根据对话上下文生成诊断假设；(ii) 通过追问澄清问题验证假设，直至得出最终诊断。为评估系统，我们采用以人物设定驱动的患者模拟器PatientSim，并结合MIMIC-IV中的患者档案，进一步调整为低特异性症状报告模式，以反映真实临床初诊中患者描述模糊的情况。实验表明，该系统在诊断准确率和效率上优于强基线，医师评估也证实了模拟器的真实性及生成追问问题的临床价值。代码将于发表后开源。
原文摘要 · Abstract (English)
Conversational diagnosis requires multi-turn history-taking, where an agent asks clarifying questions to refine differential diagnoses under incomplete information. Existing approaches often rely on the parametric knowledge of a model or assume that patients provide rich and concrete information, which is unrealistic. To address these limitations, we propose a conversational diagnosis system that explores a diagnostic knowledge graph to reason in two steps: (i) generating diagnostic hypotheses from the dialogue context, and (ii) verifying hypotheses through clarifying questions, which are repeated until a final diagnosis is reached. Since evaluating the system requires a realistic patient simulator that responds to the system's questions, we adopt PatientSim, a persona-driven patient simulator, together with patient profiles from MIMIC-IV. We further adapt it with low-specificity symptom reporting to reflect how real-world patients describe symptoms vaguely during early clinical encounters. Experiments show improved diagnostic accuracy and efficiency over strong baselines, and physician evaluations support the realism of our simulator and the clinical utility of the generated clarifying questions. Our code will be released upon publication.
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