CDE-Mapper: Using retrieval-augmented language models for linking clinical data elements to controlled vocabularies
Open accessAbstract
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. This paper presents CDE-Mapper, a framework that combines a retrieval-augmented generation strategy with large language models to automate the alignment of CDEs with 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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The Netherlands/Switzerland applied a multi-agent RAG framework to represent clinical data elements in controlled vocabularies.
“The Netherlands/Switzerland applied a multi-agent RAG framework to represent clinical data elements in controlled vocabularies.”
From Multi-Agent Retrieval Augmented Generation for Clinical Decision Support: A Systematic Review and Integrative Conceptual Framework (2026) · cites this paper as [19]
The Conclusions (p. 18) say CDE-Mapper uses RAG to link clinical data elements to controlled vocabularies. The Proposed Framework (p. 7) describes a modular, stepwise workflow, not a multi-agent framework. The paper lists Netherlands-based authors (p. 1) and a Swiss-approved dataset (2.6, p. 6), but does not describe the framework as a Netherlands/Switzerland application. (LLM, conf=0.87)
Experimental — accuracy not yet measuredVarious agents executed query decomposition, ensemble retrieval, and knowledge consolidation, which allowed for accurate mapping of atomic and composite CDEs.
“Various agents executed query decomposition, ensemble retrieval, and knowledge consolidation, which allowed for the accurate mapping of atomic and composite CDEs [19].”
From Multi-Agent Retrieval Augmented Generation for Clinical Decision Support: A Systematic Review and Integrative Conceptual Framework (2026) · cites this paper as [19]
The Introduction (p. 3) describes a modular RAG architecture with query decomposition, ensemble retrieval, knowledge filtering, and a knowledge reservoir; Performance by CDE Type (p. 14) reports accurate mapping of both atomic and composite CDEs. The source describes these as framework components, not as tasks executed by various agents, and calls the storage component a knowledge reservoir rather than knowledge consolidation. (LLM, conf=0.85)
Experimental — accuracy not yet measured[19] adopted a multi-agent architecture combining query decomposition, ensemble retrieval, and verification to achieve a 7.2% improvement in accuracy over traditional terminological mappers.
“[19] Adopted multiagent architecture combined query decomposition, ensemble retrieval, and verification to achieve a 7.2% improvement to accuracy in their mapping over traditional terminological mappers.”
From Multi-Agent Retrieval Augmented Generation for Clinical Decision Support: A Systematic Review and Integrative Conceptual Framework (2026) · cites this paper as [19]
The Conclusions (p. 18) describe a modular RAG framework combining query decomposition, ensemble retrieval, knowledge filtering, and two-step reranking; the Discussion (p. 18) also describes automated and expert validation. Table 3 (p. 13) reports higher accuracy than baseline models, but our copy does not call the framework a multi-agent architecture or report a 7.2% accuracy improvement over traditional terminological mappers. (LLM, conf=0.89)
Experimental — accuracy not yet measuredMulti-agent frameworks for clinical data mapping achieved a 7.2% accuracy gain over rule-based methods.
“while multi-agent frameworks for clinical data mapping achieved a 7.2% accuracy gain over rule-based methods [19]”
From Multi-Agent Retrieval Augmented Generation for Clinical Decision Support: A Systematic Review and Integrative Conceptual Framework (2026) · cites this paper as [19]
The Abstract (p. 1) reports a 7.2% average accuracy improvement for CDE-Mapper across four datasets. It describes CDE-Mapper as a retrieval-augmented language-model framework, not a multi-agent framework. The performance comparison (p. 13) identifies the baselines as models including SapBERT, KRISS BERT, and BioBERT-snomed, not rule-based methods. (LLM, conf=0.90)
Experimental — accuracy not yet measured
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Guideline-grounded Retrieval-augmented Generation for Ophthalmic Clinical Decision Support
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España-Lopera, J. C., Núñez‐Valdéz, E. R., & García-Díaz, V. (2026) · International Conference on Federated Learning and Intelligent Computing Systems (FLICS) · doi:10.1109/flics70075.2026.11621915
Yu, S., Gu, X., Gao, Y., Zhao, F., & Zhang, J. (2026) · Integrated Diagnostics · doi:10.1016/j.intdia.2026.100006
Wahul, R., Kale, V. V., Parlewar, P. K., Shinde, A., Dharwadkar, S. N., Bhole, C., Jagtap, V. S., Gajare, M. P., Pole, G. S., Bhardwaj, R., et al. (2026) · Discover Computing · doi:10.1007/s10791-026-10771-0
- Authors
- Gilani, K., Verket, M., Peters, C., Dumontier, M., Rocca, H.-P. B.-L., & Urovi, V.
- Affiliations
- Maastricht University (NL)
- Universitätsklinikum Aachen (DE)
- Corresponding author
- Gilani, Komal
- ORCID
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2025-09
- Volume
- 196
- Issue
- Pt B
- Pages
- 110745
- ISSN
- 0010-4825 (ISSN-L)
- OpenAlex
- W4412739501
- Cited by
- 5
- References
- 78
- Citations by year
- 2026: 5
- FWCI
- 1.50
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Computers in Biology and Medicine (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Publications (Maastricht University) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by)
- RWTH Publications (RWTH Aachen) (submittedVersion, other-oa)
- arXiv (Cornell University) (cc-by)
- Funders
- IHI JU (101112022)
- European Federation of Pharmaceutical Industries and Associations
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Keywords
- clinical data elements, controlled vocabulary, retrieval-augmented generation, large language models, concept linking, query decomposition, prompt engineering, in-context learning, human-in-the-loop validation, clinical data interoperability, data harmonization, clinical decision support
- MeSH terms
- Humans, Natural Language Processing, Information Storage and Retrieval, Vocabulary, Controlled, Electronic Health Records
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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