用大模型从病历中自动生成精准问诊清单，提升诊疗准备效率。
From EMR Data to Clinical Insight: An LLM-Driven Framework for Automated Pre-Consultation Questionnaire Generation
- 分三阶段提取病历事实、构建疾病因果网络、生成个性化问诊题
- 信息覆盖率达92.3%，专家评估理解度与诊断相关性显著提升
- 适合临床智能辅助系统开发人员参考
预问诊是高效医疗交付的关键环节。然而，从复杂海量的电子病历（EMRs）中生成全面的预问诊问卷仍具挑战性。直接使用大语言模型（LLM）的方法在信息完整性、逻辑顺序和疾病层面整合方面存在不足。为此，我们提出一种新型多阶段LLM驱动框架：第一阶段从病历中提取原子断言（带时间戳的关键事实）；第二阶段通过聚类病历语料库中的代表性网络，构建个人因果网络并合成疾病知识；第三阶段基于这些结构化表示生成个性化的个人问卷和标准化的疾病特异性问卷。该框架通过显式构建临床知识，克服了直接方法的局限。在真实世界病历数据集上评估，并经临床专家验证，本方法在信息覆盖率、诊断相关性、可理解性和生成时间方面表现更优，展现出提升患者信息采集的实际潜力。
原文摘要 · Abstract (English)
Pre-consultation is a critical component of effective healthcare delivery. However, generating comprehensive pre-consultation questionnaires from complex, voluminous Electronic Medical Records (EMRs) is a challenging task. Direct Large Language Model (LLM) approaches face difficulties in this task, particularly regarding information completeness, logical order, and disease-level synthesis. To address this issue, we propose a novel multi-stage LLM-driven framework: Stage 1 extracts atomic assertions (key facts with timing) from EMRs; Stage 2 constructs personal causal networks and synthesizes disease knowledge by clustering representative networks from an EMR corpus; Stage 3 generates tailored personal and standardized disease-specific questionnaires based on these structured representations. This framework overcomes limitations of direct methods by building explicit clinical knowledge. Evaluated on a real-world EMR dataset and validated by clinical experts, our method demonstrates superior performance in information coverage, diagnostic relevance, understandability, and generation time, highlighting its practical potential to enhance patient information collection.
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