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Stud Health Technol Inform. 2017;245:920-924.

Interoperability of Medication Classification Systems: Lessons Learned Mapping Established Pharmacologic Classes (EPCs) to SNOMED CT.

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Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
Apelon Inc, Harford, CT, USA.
Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
Stanford Center for Biomedical Informatics Research, Stanford, CA, USA.
National Library of Medicine, Bethesda, MD, USA.


Interoperability among medication classification systems is known to be limited. We investigated the mapping of the Established Pharmacologic Classes (EPCs) to SNOMED CT. We compared lexical and instance-based methods to an expert-reviewed reference standard to evaluate contributions of these methods. Of the 543 EPCs, 284 had an equivalent SNOMED CT class, 205 were more specific, and 54 could not be mapped. Precision, recall, and F1 score were 0.416, 0.620, and 0.498 for lexical mapping and 0.616, 0.504, and 0.554 for instance-based mapping. Each automatic method has strengths, weaknesses, and unique contributions in mapping between medication classification systems. In our experience, it was beneficial to consider the mapping provided by both automated methods for identifying potential matches, gaps, inconsistencies, and opportunities for quality improvement between classifications. However, manual review by subject matter experts is still needed to select the most relevant mappings.


Pharmaceutical Databases; Topical

[Indexed for MEDLINE]
Free PMC Article

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