MCN: A comprehensive corpus for medical concept normalization

Journal of Biomedical Informatics
Yen-Fu LuoAnna Rumshisky

Abstract

Normalization of clinical text involves linking different ways of talking about the same clinical concept to the same term in the standardized vocabulary. To date, very few annotated corpora for normalization have been available, and existing corpora so far have been limited in scope and only dealt with the normalization of diseases and disorders. In this paper, we describe the annotation methodology we developed in order to create a new manually annotated wide-coverage corpus for clinical concept normalization, the Medical Concept Normalization (MCN) corpus. In order to ensure wider coverage, we applied normalization to the text spans corresponding to the medical problems, treatments, and tests in the named entity corpus released for the fourth i2b2/VA shared task. In contrast to previous annotation efforts, we do not assign multiple concept labels to the named entities that do not map to a unique concept in the controlled vocabulary. Nor do we leave that named entity without a concept label. Instead, our normalization method that splits such named entities, resolving some of the core ambiguity issues. Lastly, we supply a sieve-based normalization baseline for MCN which combines MetaMap with multiple exact match components. Th...Continue Reading

Citations

Jan 30, 2020·BMC Bioinformatics·Jin-Woo ChungJong C Park
Oct 9, 2020·Journal of the American Medical Informatics Association : JAMIA·Long ChenYang Huang
Dec 16, 2020·Journal of the American Medical Informatics Association : JAMIA·Denis Newman-GriffisEric Fosler-Lussier
Aug 15, 2021·Journal of Biomedical Informatics·Shikhar VashishthCarolyn P Rosé
Sep 2, 2021·Annual Review of Biomedical Data Science·Bethany Percha
Sep 24, 2021·Bioinformatics·Kishlay Jha, Aidong Zhang
Oct 5, 2020·Journal of Biomedical Informatics·Rohit J Kate

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