用大模型自动把医学条件转成SQL，精准找患者队列
Generating patient cohorts from electronic health records using two-step retrieval-augmented text-to-SQL generation
- 两阶段检索增强生成，结合医学知识库解析复杂条件
- 在EHR数据上达0.75的F1分数，准确捕捉时间与逻辑关系
- 适合临床研究者快速构建研究队列，节省人工编码时间
临床队列定义对患者招募和观察性研究至关重要，但将纳入/排除标准转化为SQL查询仍具挑战且依赖人工。本文提出一种自动化系统，利用大语言模型结合条件解析、两级检索增强生成、医学概念标准化及SQL生成，从电子健康记录中检索患者队列并构建患者流程。该系统在EHR数据上的队列识别任务中取得0.75的F1分数，有效捕获复杂的时序与逻辑关系。结果表明，该方法在流行病学研究中具备自动化队列生成的可行性。
原文摘要 · Abstract (English)
Clinical cohort definition is crucial for patient recruitment and observational studies, yet translating inclusion/exclusion criteria into SQL queries remains challenging and manual. We present an automated system utilizing large language models that combines criteria parsing, two-level retrieval augmented generation with specialized knowledge bases, medical concept standardization, and SQL generation to retrieve patient cohorts with patient funnels. The system achieves 0.75 F1-score in cohort identification on EHR data, effectively capturing complex temporal and logical relationships. These results demonstrate the feasibility of automated cohort generation for epidemiological research.
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