利用电子健康记录进行流行病学问答的检索增强型 text-to-SQL 生成
Retrieval augmented text-to-SQL generation for epidemiological question answering using electronic health records
- Bayer AG（拜耳公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出结合医学编码与 RAG 的端到端 text-to-SQL 方法，用于基于 EHR 和理赔数据回答流行病学问题，并在真实行业场景中验证其优于简单提示。
AI中文摘要：
电子健康记录（EHR）和理赔数据是反映患者健康状况及医疗保健利用情况的丰富真实世界数据来源。由于医学术语的复杂性以及对复杂 SQL 查询的需求，查询这些数据库以回答流行病学问题颇具挑战。在此，我们提出一种端到端方法，将 text-to-SQL 生成与检索增强生成（RAG）相结合，利用 EHR 和理赔数据回答流行病学问题。我们表明，该方法将医学编码步骤集成到 text-to-SQL 流程中，相比简单提示显著提升了性能。我们的研究结果表明，尽管当前语言模型在无监督使用方面的准确性仍显不足，但正如在真实行业场景中所展示的，RAG 为提升其能力提供了一个有前景的方向。
英文摘要：
Electronic health records (EHR) and claims data are rich sources of real-world data that reflect patient health status and healthcare utilization. Querying these databases to answer epidemiological questions is challenging due to the intricacy of medical terminology and the need for complex SQL queries. Here, we introduce an end-to-end methodology that combines text-to-SQL generation with retrieval augmented generation (RAG) to answer epidemiological questions using EHR and claims data. We show that our approach, which integrates a medical coding step into the text-to-SQL process, significantly improves the performance over simple prompting. Our findings indicate that although current language models are not yet sufficiently accurate for unsupervised use, RAG offers a promising direction for improving their capabilities, as shown in a realistic industry setting.
