Faculty, Staff and Student Publications

Language

English

Publication Date

7-1-2025

Journal

Journal of the American Medical Informatics Association

DOI

10.1093/jamia/ocaf064

PMID

40332956

PMCID

PMC12202029

PubMedCentral® Posted Date

5-7-2025

PubMedCentral® Full Text Version

Post-print

Abstract

Objective: Common Data Elements (CDEs) standardize data collection and sharing across studies, enhancing data interoperability and improving research reproducibility. However, implementing CDEs presents challenges due to the broad range and variety of data elements. This study aims to develop a CDE mapping tool to bridge the gap between local data elements and National Institutes of Health (NIH) CDEs.

Methods: We propose CDEMapper, a large language model (LLM)-powered mapping tool designed to assist in mapping local data elements to NIH CDEs. CDEMapper has 3 core modules: (1) CDE indexing and embeddings. NIH CDEs were indexed and embedded to support semantic search; (2) CDE recommendations. The tool combines Elasticsearch (BM25 methods) with GPT services to recommend candidate CDEs and their permissible values; and (3) Human review. Users review and select the best match for their data elements and value sets. We evaluate the tool's recommendation accuracy and usability against manual annotations and testing.

Results: CDEMapper offers a publicly available, LLM-powered, and intuitive user interface that consolidates essential and advanced mapping services into a streamlined pipeline. The evaluation results demonstrated that the augmented BM25 with GPT embeddings and a GPT ranker achieved the overall best performance. The usability test also highlighted the effectiveness and efficiency of our tool.

Discussions and conclusions: This work opens up the potential of using LLMs to assist with CDE mapping when aligning local data elements with NIH CDEs. Additionally, this effort helps researchers better understand the gaps between their data elements and NIH CDEs while promoting CDE reusability.

Keywords

National Institutes of Health (U.S.), United States, Common Data Elements, Humans, Software, Semantics, Large Language Models, common data element, interoperability, data collection, data sharing, large language model

Published Open-Access

yes

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