用大模型自动匹配临床数据项与标准术语，提升医疗数据互通性。
CDE-Mapper: Using Retrieval-Augmented Language Models for Linking Clinical Data Elements to Controlled Vocabularies
- 通过查询分解和提示工程，处理复杂临床数据结构
- 在四个数据集上比基线方法平均高7.2%准确率
- 适合医疗数据标准化、临床研究系统开发者使用
临床数据元素（CDEs）的标准化旨在确保不同医疗系统间患者信息的一致性和完整性。现有方法在处理表示形式多样、结构复杂的CDEs时表现不佳，阻碍了临床研究中的数据整合与互操作性。我们提出CDE-Mapper，一种基于检索增强生成与大语言模型的创新框架，用于自动化将CDEs链接至受控词汇表。该模块化方法采用查询分解以应对不同复杂度的CDEs，将专家定义规则融入提示工程，并结合上下文学习与多重检索器组件解决术语歧义。此外，我们构建了一个经人机协作验证的知识库，实现未来应用中高精度的概念链接，同时降低计算成本。在四个异构数据集上，CDE-Mapper相比基线方法平均提升7.2%的准确率。本研究展示了先进语言模型在提升数据同质化方面的潜力，显著增强了临床决策支持系统与科研能力。
原文摘要 · Abstract (English)
The standardization of clinical data elements (CDEs) aims to ensure consistent and comprehensive patient information across various healthcare systems. Existing methods often falter when standardizing CDEs of varying representation and complex structure, impeding data integration and interoperability in clinical research. We introduce CDE-Mapper, an innovative framework that leverages Retrieval-Augmented Generation approach combined with Large Language Models to automate the linking of CDEs to controlled vocabularies. Our modular approach features query decomposition to manage varying levels of CDEs complexity, integrates expert-defined rules within prompt engineering, and employs in-context learning alongside multiple retriever components to resolve terminological ambiguities. In addition, we propose a knowledge reservoir validated by a human-in-loop approach, achieving accurate concept linking for future applications while minimizing computational costs. For four diverse datasets, CDE-Mapper achieved an average of 7.2\% higher accuracy improvement compared to baseline methods. This work highlights the potential of advanced language models in improving data harmonization and significantly advancing capabilities in clinical decision support systems and research.
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