标题： 在环境约束下通过大型语言模型可靠地整理电子健康记录数据集 显示英文标题

标题： Reliable Curation of EHR Dataset via Large Language Models under Environmental Constraints

摘要： 电子健康记录（EHRs）是现代医疗保健和研究的核心；然而，许多研究人员缺乏编写复杂SQL查询或生成有效可视化所需的数据库专业知识，这限制了数据的有效使用和科学发现。 为解决这一障碍，我们引入CELEC，这是一个基于大型语言模型（LLM）的自动化EHR数据提取和分析框架。 CELEC使用一种集成模式信息、少量示例和思维链推理的提示策略，将自然语言查询转换为SQL，这三者共同提高了准确性和鲁棒性。 在EHRSQL基准的一个子集上，CELEC的执行准确性与先前系统相当，同时通过仅向LLM暴露数据库元数据来保持低延迟、成本效率和严格的隐私保护。 CELEC还遵循严格的隐私协议：LLM仅访问数据库元数据（例如，表名和列名），而所有查询执行都在机构环境中安全进行，确保没有任何患者级别的数据被传输到或与LLM共享。 消融研究表明，SQL生成管道的每个组件，尤其是少量示例，在性能中起着关键作用。 通过降低技术障碍并使医学研究人员能够直接查询EHR数据库，CELEC简化了研究流程并加速了生物医学发现。 显示英文摘要

摘要： Electronic health records (EHRs) are central to modern healthcare delivery and research; yet, many researchers lack the database expertise necessary to write complex SQL queries or generate effective visualizations, limiting efficient data use and scientific discovery. To address this barrier, we introduce CELEC, a large language model (LLM)-powered framework for automated EHR data extraction and analytics. CELEC translates natural language queries into SQL using a prompting strategy that integrates schema information, few-shot demonstrations, and chain-of-thought reasoning, which together improve accuracy and robustness. On a subset of the EHRSQL benchmark, CELEC achieves execution accuracy comparable to prior systems while maintaining low latency, cost efficiency, and strict privacy by exposing only database metadata to the LLM. CELEC also adheres to strict privacy protocols: the LLM accesses only database metadata (e.g., table and column names), while all query execution occurs securely within the institutional environment, ensuring that no patient-level data is ever transmitted to or shared with the LLM. Ablation studies confirm that each component of the SQL generation pipeline, particularly the few-shot demonstrations, plays a critical role in performance. By lowering technical barriers and enabling medical researchers to query EHR databases directly, CELEC streamlines research workflows and accelerates biomedical discovery.