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CC BY-NC-ND 4.0 · Methods Inf Med 2025; 64(01/02): 031-039
DOI: 10.1055/a-2797-4219
Original Article

Clinical Terminology Mapping Service Based on Information Retrieval

Authors

  • Sungwon Jung

    1   InfoClinic Co., Seoul, Korea
    2   Kangwon Institute of Telecommunications & Information, Kangwon National University, Chuncheon, Korea

  • Seung-Jong Yu

    1   InfoClinic Co., Seoul, Korea

  • Byoung-Kee Yi

    3   Department of Artificial Intelligence Convergence, Kangwon National University, Chuncheon, Korea

Funding Information This research was supported by the Regional Innovation System & Education (RISE) program through the Gangwon RISE Center, funded by the Ministry of Education (MOE) and the Gangwon State (G.S.), Republic of Korea (2025-RISE-10-005). This study was supported by 2023 Research Grant from Kangwon National University (202305120001).
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Abstract

Background

Standardized clinical terminology is essential for semantic interoperability. Typically, a hospital's terminology expert manually maps local terminology with international standards such as SNOMED CT. The manual mapping process is demanding, labor-intensive, and time-consuming, and its effectiveness relies on the expertise of the professional handling it.

Objective

We developed a method to map clinical terms to SNOMED CT concept descriptions using an information retrieval (IR) approach with rich synonyms. We also provide a free mapping support service to help terminology experts alleviate the challenges of manual mapping without the need for additional manipulation.

Methods

We created indexes using edge n-grams and synonyms. We adopted Elasticsearch for indexing and query processing, incorporating data from the SPECIALIST Lexicon to enrich the synonym database. Eight different indexes were initially created, but only four were retained based on performance. We tested indexes individually and in combination, using a dataset of 1,753 one-to-one mapped instances from the National Library of Medicine ICD-9-CM Procedure codes to the SNOMED CT Map. We compared our approach with MetaMap for evaluation.

Results

We found that using rich synonyms and edge n-gram indexing significantly improved the accuracy of mapping clinical terms to SNOMED CT. The indexes incorporating synonyms and edge n-grams performed better than those using either technique alone. Combining these methods captured more relevant terms and synonyms, resulting in more precise mappings. Our method outperformed the baseline provided by MetaMap, demonstrating enhanced capability in handling complex medical terminology and improving the overall mapping quality.

Conclusion

Our study introduced an IR method with rich synonyms for mapping clinical terms to SNOMED CT, analyzing 40 unmapped terms, and identifying key issues. The approach shows promise in improving terminology mapping, and future work will explore advanced methods to enhance accuracy further, aiming to reduce manual mapping efforts and improve result evaluation.

Keywords

semantic interoperability - term mapping - information retrieval - rich synonym - query expansion


Publication History

Received: 27 August 2025

Accepted: 23 January 2026

Accepted Manuscript online:
03 February 2026

Article published online:
16 February 2026

© 2026. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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