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Automatic Discovery of Regular Expression Patterns Representing Negated Findings in Medical Narrative Reports

  • Roni Romano
  • Lior Rokach
  • Oded Maimon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4032)

Abstract

Substantial medical data such as discharge summaries and operative reports are stored in textual form. Databases containing free-text clinical narratives reports often need to be retrieved to find relevant information for clinical and research purposes. Terms that appear in these documents tend to appear in different contexts. The context of negation, a negative finding, is of special importance, since many of the most frequently described findings are those denied by the patient or subsequently “ruled out.” Hence, when searching free-text narratives for patients with a certain medical condition, if negation is not taken into account, many of the documents retrieved will be irrelevant. In this paper we examine the applicability of a new pattern learning method for automatic identification of negative context in clinical narratives reports. We compare the new algorithm to previous methods proposed for the same task of similar medical narratives and show its advantages. The new algorithm can be applied also to further context identification and information extraction tasks.

Keywords

Medical Informatics Text Classification Machine Learning Information Retrieval 

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References

  1. 1.
    Aronow, D., Feng, F., Croft, W.B.: Ad Hoc Classification of Radiology Reports. Journal of the American Medical Informatics Association 6(5), 393–411 (1999)Google Scholar
  2. 2.
    Averbuch, M., Karson, T., Ben-Ami, B., Maimon, O., Rokach, L.: Context-Sensitive Medical Information Retrieval. In: MEDINFO 2004, San Francisco, CA, September 2004, pp. 282–286. IOS Press, Amsterdam (2004)Google Scholar
  3. 3.
    Cessie, S., van Houwelingen, J.C.: Ridge Estimators in Logistic Regression. Applied Statistics 41(1), 191–201 (1997)CrossRefGoogle Scholar
  4. 4.
    Chapman, W.W., Bridewell, W., Hanbury, P., Cooper, G.F., Buchanann, B.G.: A Simple Algorithm for Identifying Negated Findings and Diseases in Discharge Summaries. J. Biomedical Info. 34, 301–310 (2001)CrossRefGoogle Scholar
  5. 5.
    Duda, R., Hart, P.: Pattern Classification and Scene Analysis. Wiley, New York (1973)zbMATHGoogle Scholar
  6. 6.
    Fiszman, M., Chapman, W.W., Aronsky, D., Evans, R.S., Haug, P.J.: Automatic detection of acute bacterial pneumonia from chest X-ray reports. J. Am. Med. Inform. Assoc. 7, 593–604 (2000)Google Scholar
  7. 7.
    Fiszman, M., Haug, P.J.: Using medical language processing to support real-time evaluation of pneumonia guidelines. In: Proc. AMIA Symp., pp. 235–239 (2000)Google Scholar
  8. 8.
    Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Machine Learning: Proceedings of the Thirteenth International Conference, pp. 325–332 (1996)Google Scholar
  9. 9.
    Friedman, C., Alderson, P., Austin, J., Cimino, J., Johnson, S.: A General Natural-Language Text Processor for Clinical Radiology. Journal of the American Medical Informatics Association 1(2), 161–174 (1994)Google Scholar
  10. 10.
    Hall, M.: Correlation- based Feature Selection for Machine Learning. Ph.D. Thesis, University of Waikato (1999)Google Scholar
  11. 11.
    Hersh, W.R., Hickam, D.H.: Information retrieval in medicine: the SAPHIRE experience. J. of the Am. Society of Information Science 46, 743–747 (1995)CrossRefGoogle Scholar
  12. 12.
    Hripcsak, G., Knirsch, C.A., Jain, N.L., Stazesky, R.C., Pablos-mendez, A., Fulmer, T.: A health in-formation network for managing innercity tuberculosis: bridging clinical care, public health, and home care. Comput. Biomed. Res. 32, 67–76 (1999)CrossRefGoogle Scholar
  13. 13.
    Keerthi, S.S., Shevade, S.K., Bhattacharyya, C., Murth, K.R.K.: Improvements to Platt’s SMO Algorithm for SVM Classifier Design. Neural Computation 13(3), 637–649 (2001)zbMATHCrossRefGoogle Scholar
  14. 14.
    Lindbergh, D.A.B., Humphreys, B.L.: The Unified Medical Language System. In: van Bemmel, J.H., McCray, A.T. (eds.) Yearbook of Medical Informatics, pp. 41–51. IMIA, Netherlands (1993)Google Scholar
  15. 15.
    Mutalik, P.G., Deshpande, A., Nadkarni, P.M.: Use of general-purpose negation detection to augment concept indexing of medical documents: a quantitative study using the UMLS. J. Am. Med. Inform. Assoc. 8(6), 598–609 (2001)Google Scholar
  16. 16.
    Myers, E.: An O(ND) difference algorithm and its variations. Algorithmica 1(2), 251 (1986)zbMATHCrossRefMathSciNetGoogle Scholar
  17. 17.
    Nadkarni, P.: Information retrieval in medicine: overview and applications. J. Postgraduate Med. 46(2) (2000)Google Scholar
  18. 18.
    Pratt, A.W.: Medicine, computers, and linguistics. Advanced Biomedical Engineering 3, 97–140 (1973)MathSciNetGoogle Scholar
  19. 19.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)Google Scholar
  20. 20.
    Rokach, L., Averbuch, M., Maimon, O.: Information Retrieval System for Medical Narrative Reports. In: Christiansen, H., Hacid, M.-S., Andreasen, T., Larsen, H.L. (eds.) FQAS 2004. LNCS (LNAI), vol. 3055, pp. 217–228. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  21. 21.
    Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Roni Romano
    • 1
  • Lior Rokach
    • 2
  • Oded Maimon
    • 1
  1. 1.Department of Industrial EngineeringTel Aviv UniversityRamat AvivIsrael
  2. 2.Department of Information Systems EngineeringBen-Gurion University of the NegevBeer-ShevaIsrael

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