Unified medical Language System term occurrences in clinical notes: A large-scale corpus analysis

Journal of the American Medical Informatics Association

Volumn 19, Issue E1, 2012, Pages

  Wu, Stephen T ^a   Liu, Hongfang ^a   Li, Dingcheng ^a   Tao, Cui ^a   Musen, Mark A ^b   Chute, Christopher G ^a   Shah, Nigam H ^b  

^a MAYO CLINIC   (United States)

^b STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; HUMAN; LANGUAGE; LINGUISTICS; NOMENCLATURE; UNIFIED MEDICAL LANGUAGE SYSTEM;

ALGORITHMS; ELECTRONIC HEALTH RECORDS; NATURAL LANGUAGE PROCESSING; SEMANTICS; UNIFIED MEDICAL LANGUAGE SYSTEM; VOCABULARY, CONTROLLED;

EID: 84863537188     PISSN: 10675027     EISSN: 1527974X     Source Type: Journal    
DOI: 10.1136/amiajnl-2011-000744     Document Type: Article

References (23)

1. Lindberg DA, Humphreys BL, McCray AT. The Unified Medical Language System. Methods Inf Med 1993;32:281.
2. Aronson AR, Lang FM. An overview of MetaMap: historical perspective and recent advances. J Am Med Inform Assoc 2010;17:229-36.
3. Savova GK, Masanz JJ, Ogren PV, et al. Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. J Am Med Inform Assoc 2010;17:507-13.
4. Parai GK, Jonquet C, Xu R, et al. The Lexicon Builder Web service: building custom lexicons from two hundred biomedical ontologies. AMIA Annu Symp Proc 2010;2010:587-91.
5. Aho AV, Corasick MJ. Efficient string matching: an aid to bibliographic search. Commun ACM 1975;18:333-40.
6. Dai M, Shah NH, Xuan W, et al. An efficient solution for mapping free text to ontology terms. AMIA Summit on Translational Bioinformatics, San Francisco, CA, 2008.
7. Savova G, Kipper-Schuler K, Buntrock J, et al. UIMA-Based Clinical Information Extraction System. Towards Enhanced Interoperability for Large HLT Systems: UIMA for NLP. Proceedings paper, LREC (Languages Resources and Evaluation Conference), Marrakech, Morocco, 2008:39.
8. Uzuner O, South BR, Shen S, et al. 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text. J Am Med Inform Assoc 2011;18:552-6.
9. Aronson A. Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. Proc AMIA Symp 2001:17-21.
10. Nadkarni P, Chen R, Brandt C. UMLS concept indexing for production databases: a feasibility study. J Am Med Inform Assoc 2001;8:80-91.
11. Denny JC, Smithers JD, Miller RA, et al. 'Understanding' medical school curriculum content using KnowledgeMap. J Am Med Inform Assoc. 2003;10:351-62.
12. Wu S, Liu H. Semantic characteristics of NLP-extracted concepts in clinical notes vs. biomedical literature. Annual Symposium of American Medical Informatics Association, Washington DC, WA, 2011.
13. Aronson AR. Filtering the UMLS Metathesaurus for MetaMap. National Library of Medicine Technical Report. 2006.
14. Halevy A, Norvig P, Pereira F. The unreasonable effectiveness of data. Intelligent Systems IEEE 2009;24:8-12.
15. McCray AT, Bodenreider O, Malley JD, et al. Evaluating UMLS strings for natural language processing. Proc AMIA Symp 2001:448.
16. Hettne KM, van Mulligen EM, Schuemie MJ, et al. Rewriting and suppressing UMLS terms for improved biomedical term identification. J Biomed Semantics 2010;1:5.
17. Thompson P, McNaught J, Montemagni S, et al. The BioLexicon: a large-scale terminological resource for biomedical text mining. BMC Bioinformatics 2011;12:397.
18. Lippincott T, Seaghdha D, Sun L, et al. Exploring variations across biomedical subdomains. Proceedings of International Conference on Computational Linguistics, Beijing, China, 2010:689-97.
19. Cohen KB, Johnson HL, Verspoor K, et al. The structural and content aspects of abstracts versus bodies of full text journal articles are different. BMC Bioinformatics 2010;11:492.
20. Xu R, Musen MA, Shah NH. A comprehensive analysis of five million UMLS metathesaurus terms using eighteen million MEDLINE citations. AMIA Annu Symp Proc 2010;2010:907-11.
21. Michel JB, Shen YK, Aiden AP, et al. Quantitative analysis of culture using millions of digitized books. Science 2011;331:176-82.
22. Chute CG, Beck SA, Fisk TB, et al. The enterprise data trust at Mayo Clinic: a semantically integrated warehouse of biomedical data. J Am Med Inform Assoc 2010;17:131.
23. Bodenreider O, McCray AT. Exploring semantic groups through visual approaches. J Biomed Inform 2003;36:414-32.