CDE-Mapper: Using Retrieval-Augmented Language Models for Linking Clinical Data Elements to Controlled Vocabularies

TL;DR

CDE-Mapper is a novel framework that uses retrieval-augmented language models to accurately link clinical data elements to controlled vocabularies, enhancing data standardization and interoperability in healthcare.

Contribution

It introduces a retrieval-augmented generation approach with query decomposition, expert rule integration, and human-in-the-loop validation for improved clinical data element linking.

Findings

Achieved 7.2% higher accuracy than baseline methods.

Validated effectiveness across four diverse datasets.

Reduced computational costs with knowledge reservoir.

Abstract

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.