让医疗对话系统学会识别并纠正患者误报症状。
Listening to Patients: A Framework of Detecting and Mitigating Patient Misreport for Medical Dialogue Generation
- 构建对话实体图，用图熵检测患者症状报告异常。
- 通过设计追问问题，有效缓解误报对诊疗的影响。
- 适用于需要高可靠性医疗问答的对话系统开发。
医疗对话系统旨在通过患者与智能体的对话提供自动化健康支持。以往研究通常假设患者是理想用户——能准确、一致地描述自身健康状况。然而现实中，患者常存在症状误报，导致其陈述与实际健康状况不符。忽略此问题会显著降低医疗咨询质量。为此，我们提出PaMis框架，使医疗对话系统能够“倾听患者”，解决误报检测与缓解两大挑战。该框架首先构建对话实体图，基于图熵检测患者误报行为，并通过生成澄清性问题来修正误报。实验表明，PaMis显著提升了医疗应答生成质量，使GPT-4等模型具备误报识别与应对能力，提供更高质量的医疗辅助。
原文摘要 · Abstract (English)
Medical Dialogue Systems aim to provide automated healthcare support through patient-agent conversations. Previous efforts typically regard patients as ideal users -- one who accurately and consistently reports their health conditions. However, in reality, patients often misreport their symptoms, leading to discrepancies between their reports and actual health conditions. Overlooking patient misreport will affect the quality of healthcare consultations provided by MDS. To address this issue, we argue that MDS should ''listen to patients'' and tackle two key challenges: how to detect and mitigate patient misreport effectively. In this work, we propose PaMis, a framework of detecting and mitigating Patient Misreport for medical dialogue generation. PaMis first constructs dialogue entity graphs, then detects patient misreport based on graph entropy, and mitigates patient misreport by formulating clarifying questions. Experiments indicate that PaMis effectively enhances medical response generation, enabling models like GPT-4 to detect and mitigate patient misreports, and provide high-quality healthcare assistance.
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