Literature DB >> 32940692

Assessing the enrichment of dietary supplement coverage in the Unified Medical Language System.

Jake Vasilakes1,2, Anusha Bompelli1, Jeffrey R Bishop3, Terrence J Adam1,2, Olivier Bodenreider4, Rui Zhang1,2.   

Abstract

OBJECTIVE: We sought to assess the need for additional coverage of dietary supplements (DS) in the Unified Medical Language System (UMLS) by investigating (1) the overlap between the integrated DIetary Supplements Knowledge base (iDISK) DS ingredient terminology and the UMLS and (2) the coverage of iDISK and the UMLS over DS mentions in the biomedical literature.
MATERIALS AND METHODS: We estimated the overlap between iDISK and the UMLS by mapping iDISK to the UMLS using exact and normalized strings. The coverage of iDISK and the UMLS over DS mentions in the biomedical literature was evaluated via a DS named-entity recognition (NER) task within PubMed abstracts.
RESULTS: The coverage analysis revealed that only 30% of iDISK terms can be matched to the UMLS, although these cover over 99% of iDISK concepts. A manual review revealed that a majority of the unmatched terms represented new synonyms, rather than lexical variants. For NER, iDISK nearly doubles the precision and achieves a higher F1 score than the UMLS, while maintaining a competitive recall. DISCUSSION: While iDISK has significant concept overlap with the UMLS, it contains many novel synonyms. Furthermore, almost 3000 of these overlapping UMLS concepts are missing a DS designation, which could be provided by iDISK. The NER experiments show that the specialization of iDISK is useful for identifying DS mentions.
CONCLUSIONS: Our results show that the DS representation in the UMLS could be enriched by adding DS designations to many concepts and by adding new synonyms.
© The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Keywords:  dietary supplements; named entity recognition, natural language processing; terminology; unified medical language system

Mesh:

Year:  2020        PMID: 32940692      PMCID: PMC7566420          DOI: 10.1093/jamia/ocaa128

Source DB:  PubMed          Journal:  J Am Med Inform Assoc        ISSN: 1067-5027            Impact factor:   4.497


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