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CC BY 4.0 · Appl Clin Inform 2025; 16(04): 1263-1270
DOI: 10.1055/a-2606-9411
Research Article

Semantic Relations: Extending SNOMED CT and Solor

Authors

  • Melissa P. Resnick

    1   Department of Biomedical Informatics, University at Buffalo, Buffalo, New York, United States
    2   U.S. Department of Veterans Affairs, Western New York Health Care, Buffalo, New York, United States

  • James Hitt

    1   Department of Biomedical Informatics, University at Buffalo, Buffalo, New York, United States
    3   U.S. Department of Veterans Affairs, Office of Clinical Informatics, Washington, District of Columbia, United States

  • Wilmon McCray

    1   Department of Biomedical Informatics, University at Buffalo, Buffalo, New York, United States
    2   U.S. Department of Veterans Affairs, Western New York Health Care, Buffalo, New York, United States

  • Kendria Hall 

    1   Department of Biomedical Informatics, University at Buffalo, Buffalo, New York, United States

  • Frank LeHouillier

    1   Department of Biomedical Informatics, University at Buffalo, Buffalo, New York, United States
    2   U.S. Department of Veterans Affairs, Western New York Health Care, Buffalo, New York, United States

  • Steven H. Brown

    3   U.S. Department of Veterans Affairs, Office of Clinical Informatics, Washington, District of Columbia, United States

  • Keith E. Campbell

    3   U.S. Department of Veterans Affairs, Office of Clinical Informatics, Washington, District of Columbia, United States

  • Diane Montella

    3   U.S. Department of Veterans Affairs, Office of Clinical Informatics, Washington, District of Columbia, United States

  • Jonathan Nebeker

    3   U.S. Department of Veterans Affairs, Office of Clinical Informatics, Washington, District of Columbia, United States

  • Peter L. Elkin

    1   Department of Biomedical Informatics, University at Buffalo, Buffalo, New York, United States
    2   U.S. Department of Veterans Affairs, Western New York Health Care, Buffalo, New York, United States
    3   U.S. Department of Veterans Affairs, Office of Clinical Informatics, Washington, District of Columbia, United States
    4   Faculty of Engineering, University of Southern Denmark, Odense, Denmark

Funding This work has been supported in part by grants from NIH (grant nos.: NLM T15LM012595, NIAAA R21AA026954, NIAAA R33AA026954, and NCATS UL1TR001412. [This study was funded in part by the Department of Veterans Affairs (U.S. Department of Veterans Affairs, U.S. National Institutes of Health–National Center for Advancing Translational Sciences,] grant no.: UL1TR001412); U.S. National Institutes of Health–National Institute on Alcohol Abuse and Alcoholism, [grant nos.: R21AA026954 and R33AA026954]; and U.S. National Institutes of Health–National Library of Medicine [rant no.: T15LM012595]).
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Abstract

Background

Terminologies, such as Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) and Solor, assist with knowledge representation and management, data integration, and triggering clinical decision support (CDS) rules. Semantic relations in these terminologies provide explicit meaning in compositional expressions, which assist with many of the above-listed activities.

Objective

The aims of this research are to: (1) identify semantic relations that are not fully present in SNOMED CT and Solor and (2) use these identified semantic relations with terms that are currently present in SNOMED CT and Solor to form triples.

Methods

We identified relations that were not fully present in either SNOMED CT or Solor and were important for VA Knowledge Artifacts (KNARTS). These terms and the relations were formed into triples. The relations, terms, classifications, and sentences were used to implement the relations in the High Definition-Natural Language Processing (HD-NLP) program.

Results

There are a total of 38 semantic relations. These had use cases built for each and were implemented in the Solor HD-NLP server for tagging of KNARTS.

Conclusions

These new SNOMED CT and Solor semantic relations will give clinicians the ability to add more detail and meaning to their clinical notes. This can improve our ability to trigger CDS rules, leading to improved CDS provided to clinicians during patient care.

Keywords

SNOMED CT - clinical decision support - natural language processing - semantics - patient care

Protection of Human and Animal Subjects

This research did not involve humans, as it utilized only deidentified data. No identifiable personal information was collected or analyzed. Therefore, human subjects review was not required.




Publication History

Received: 01 January 2025

Accepted: 09 May 2025

Article published online:
03 October 2025

© 2025. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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