ArticleJournal of the American Medical Informatics Association : JAMIA2012
Unified Medical Language System term occurrences in clinical notes: a large-scale corpus analysis.
Article in Journal of the American Medical Informatics Association : JAMIA, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed.
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- Predicting Future Cardiovascular Events in Patients With Peripheral Artery Disease Using Electronic Health Record Data.Circulation. Cardiovascular quality and outcomes · 2019Article
- Empirical advances with text mining of electronic health records.BMC medical informatics and decision making · 2017Article
- A Clinical Score for Predicting Atrial Fibrillation in Patients with Cryptogenic Stroke or Transient Ischemic Attack.Cardiology · 2017Article
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- Androgen Deprivation Therapy and Future Alzheimer's Disease Risk.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2016Article
- The utility of including pathology reports in improving the computational identification of patients.Journal of pathology informatics · 2016Article
- Statin Intensity or Achieved LDL? Practice-based Evidence for the Evaluation of New Cholesterol Treatment Guidelines.PloS one · 2016Article
- Comparison of UMLS terminologies to identify risk of heart disease using clinical notes.Journal of biomedical informatics · 2015Article
- Textual inference for eligibility criteria resolution in clinical trials.Journal of biomedical informatics · 2015Article
- Translational research platforms integrating clinical and omics data: a review of publicly available solutions.Briefings in bioinformatics · 2015Review
- Identifying named entities from PubMed for enriching semantic categories.BMC bioinformatics · 2015Article
- Interactive Cohort Identification of Sleep Disorder Patients Using Natural Language Processing and i2b2.Applied clinical informatics · 2015Article
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7 authors.
Funding
Abstract
objectiveTo 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.
designBased 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.
resultsFor 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 106426 and 94788 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.
conclusionThe 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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