skip to main content
10.5555/1572392.1572412dlproceedingsArticle/Chapter ViewAbstractPublication PagesbionlpConference Proceedingsconference-collections
research-article
Free Access

From indexing the biomedical literature to coding clinical text: experience with MTI and machine learning approaches

Published:29 June 2007Publication History

ABSTRACT

This paper describes the application of an ensemble of indexing and classification systems, which have been shown to be successful in information retrieval and classification of medical literature, to a new task of assigning ICD-9-CM codes to the clinical history and impression sections of radiology reports. The basic methods used are: a modification of the NLM Medical Text Indexer system, SVM, k-NN and a simple pattern-matching method. The basic methods are combined using a variant of stacking. Evaluated in the context of a Medical NLP Challenge, fusion produced an F-score of 0.85 on the Challenge test set, which is considerably above the mean Challenge F-score of 0.77 for 44 participating groups.

References

  1. Aronson AR, Demner-Fushman D, Humphrey SM, Lin J, Liu H, Ruch P, Ruiz ME, Smith LH, Tanabe LK, Wilbur WJ. Fusion of knowledge-intensive and statistical approaches for retrieving and annotating textual genomics documents. Proc TREC 2005, 36--45.Google ScholarGoogle Scholar
  2. Aronson AR, Mork JG, Gay CW, Humphrey SM and Rogers WJ. The NLM Indexing Initiative's Medical Text Indexer. Medinfo. 2004: 268--72.Google ScholarGoogle Scholar
  3. Bodenreider O, Nelson SJ, Hole WT and Chang HF. Beyond synonymy: exploiting the UMLS semantics in mapping vocabularies. Proc AMIA Symp 1998: 815--9.Google ScholarGoogle Scholar
  4. Chapman WW, Bridewell W, Hanbury P, Cooper GF, Buchanan B. Evaluation of negation phrases in narrative clinical reports. Proc AMIA Symp. 2001a:105--9.Google ScholarGoogle Scholar
  5. Chapman WW, Bridewell W, Hanbury P, Cooper GF and Buchanan BG. A simple algorithm for identifying negated findings and diseases in discharge summaries. J Biomed Inform. 2001b;34:301--10.Google ScholarGoogle Scholar
  6. Demner-Fushman D, Humphrey SM, Ide NC, Loane RF, Ruch P, Ruiz ME, Smith LH, Tanabe LK, Wilbur WJ and Aronson AR. Finding relevant passages in scientific articles: fusion of automatic approaches vs. an interactive team effort. Proc TREC 2006, 569--76.Google ScholarGoogle Scholar
  7. Fung KW and Bodenreider O. Utilizing the UMLS for semantic mapping between terminologies. AMIA Annu Symp Proc 2005: 266--70.Google ScholarGoogle Scholar
  8. Gay CW, Kayaalp M and Aronson AR. Semi-automatic indexing of full text biomedical articles. AMIA Annu Symp Proc. 2005:271--5.Google ScholarGoogle Scholar
  9. Goldin I and Chapman WW. Learning to detect negation with 'not' in medical texts. Proc Workshop on Text Analysis and Search for Bioinformatics, ACM SIGIR, 2003.Google ScholarGoogle Scholar
  10. Hunter L and Cohen KB. Biomedical language processing: what's beyond PubMed? Mol Cell. 2006 Mar 3;21(5):589--94.Google ScholarGoogle Scholar
  11. Tanabe L and Wilbur WJ. (2002) Tagging gene and protein names in biomedical text. Bioinformatics, Aug 2002; 18: 1124--32.Google ScholarGoogle Scholar
  12. Ting WK and Witten I. 1997. Stacking bagged and dagged models. 367--375. Proc. of ICML'97. Morgan Kaufmann, San Francisco, CA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  1. From indexing the biomedical literature to coding clinical text: experience with MTI and machine learning approaches

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image DL Hosted proceedings
          BioNLP '07: Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
          June 2007
          241 pages

          Publisher

          Association for Computational Linguistics

          United States

          Publication History

          • Published: 29 June 2007

          Qualifiers

          • research-article

          Acceptance Rates

          Overall Acceptance Rate33of92submissions,36%

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader