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Unified Medical Language System term occurrences in clinical notes: a large-scale corpus analysis
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Abstract:
Objective To characterise empirical instances of Unified Medical Language System (UMLS) Metathesaurus term strings in a large clinical corpus, and to illustrate what types of term characteristics are generalisable across data sources. Design Based on the occurrences of UMLS terms in a 51 million document corpus of Mayo Clinic clinical notes, this study computes statistics about the terms' string attributes, source terminologies, semantic types and syntactic categories. Term occurrences in 2010 i2b2/VA text were also mapped; eight example filters were designed from the Mayo-based statistics and applied to i2b2/VA data. Results For the corpus analysis, negligible numbers of mapped terms in the Mayo corpus had over six words or 55 characters. Of source terminologies in the UMLS, the Consumer Health Vocabulary and Systematized Nomenclature of Medicine—Clinical Terms (SNOMED-CT) had the best coverage in Mayo clinical notes at 106 426 and 94 788 unique terms, respectively. Of 15 semantic groups in the UMLS, seven groups accounted for 92.08% of term occurrences in Mayo data. Syntactically, over 90% of matched terms were in noun phrases. For the cross-institutional analysis, using five example filters on i2b2/VA data reduces the actual lexicon to 19.13% of the size of the UMLS and only sees a 2% reduction in matched terms. Conclusion The corpus statistics presented here are instructive for building lexicons from the UMLS. Features intrinsic to Metathesaurus terms (well formedness, length and language) generalise easily across clinical institutions, but term frequencies should be adapted with caution. The semantic groups of mapped terms may differ slightly from institution to institution, but they differ greatly when moving to the biomedical literature domain.
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Keyword:
Research and applications
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URL: https://doi.org/10.1136/amiajnl-2011-000744
http://jamia.bmj.com/cgi/content/short/amiajnl-2011-000744v1
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Towards a semantic lexicon for clinical natural language processing
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Entity relation detection with Factorial Hidden Markov Models and Maximum Entropy Discriminant Latent Dirichlet Allocations.
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