一种用于高效临床信息采集的基于大语言模型的两阶段主动对话生成器
A Two-Stage Proactive Dialogue Generator for Efficient Clinical Information Collection Using Large Language Model
AI总结：
针对医患信息采集效率低的问题，提出融合两阶段推荐结构、排序标准与交互式患者智能体的大语言模型驱动诊断对话系统，可多轮采集诊断信息，在真实数据集上表现出良好的流畅性、专业性与安全性。
摘要：
高效的医患互动是疾病诊断成功的关键因素之一。在对话过程中，医生可以询问补充性诊断信息，例如患者症状、既往手术史以及其他超出医学证据数据（检验结果）的相关信息，以辅助提升疾病诊断效果。然而，这一过程通常耗时且效率较低，有望通过计算机辅助系统进行优化。为此，我们提出了一套诊断对话系统，以实现患者信息采集流程的自动化。借助病史信息与对话逻辑，我们的对话智能体（尤其是医生智能体）能够发起多轮临床问询，有效采集最具相关性的疾病诊断信息。此外，得益于我们的两阶段推荐结构、精心设计的排序标准以及交互式患者智能体，本模型能够克服对话生成中探索不足与灵活性欠缺的难题。我们在真实世界医疗对话数据集上的实验结果表明，本模型能够生成模拟真实医生对话风格的临床问询，具备高效的流畅性、专业性与安全性，同时可有效采集相关疾病诊断信息。
英文摘要：
Efficient patient-doctor interaction is among the key factors for a successful disease diagnosis. During the conversation, the doctor could query complementary diagnostic information, such as the patient's symptoms, previous surgery, and other related information that goes beyond medical evidence data (test results) to enhance disease diagnosis. However, this procedure is usually time-consuming and less-efficient, which can be potentially optimized through computer-assisted systems. As such, we propose a diagnostic dialogue system to automate the patient information collection procedure. By exploiting medical history and conversation logic, our conversation agents, particularly the doctor agent, can pose multi-round clinical queries to effectively collect the most relevant disease diagnostic information. Moreover, benefiting from our two-stage recommendation structure, carefully designed ranking criteria, and interactive patient agent, our model is able to overcome the under-exploration and non-flexible challenges in dialogue generation. Our experimental results on a real-world medical conversation dataset show that our model can generate clinical queries that mimic the conversation style of real doctors, with efficient fluency, professionalism, and safety, while effectively collecting relevant disease diagnostic information.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
