CIE-9-MC Code Classification with knn and SVM

  • David Lojo
  • David E. Losada
  • Álvaro Barreiro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5602)

Abstract

This paper is concerned with automatic classification of texts in a medical domain. The process consists in classifying reports of medical discharges into classes defined by the CIE-9-MC codes. We will assign CIE-9-MC codes to reports using either a knn model or support vector machines. One of the added values of this work is the construction of the collection using the discharge reports of a medical service. This is a difficult collection because of the high number of classes and the uneven balance between classes. In this work we study different representations of the collection, different classication models, and different weighting schemes to assign CIE-9-MC codes. Our use of document expansion is particularly novel: the training documents are expanded with the descriptions of the assigned codes taken from CIE-9-MC. We also apply SVMs to produce a ranking of classes for each test document. This innovative use of SVM offers good results in such a complicated domain.

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References

  1. 1.
    Aas, K., Eikvil, L.: Text categorisation: A survey. Technical report, Norwegian Computing Center (1999)Google Scholar
  2. 2.
    Hsu, C.W., Lin, C.J.: A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks 13(2) (2002)Google Scholar
  3. 3.
    Joachims, T.: Making large-scale svm learning practical. In: Advances in Kernel Methods - Support Vector Learning. MIT press, Cambridge (1999)Google Scholar
  4. 4.
    Larkey, L., Croft, W.B.: Automatic assignment of cie-9 codes to discharge summaries. Technical report, CIIR (1995)Google Scholar
  5. 5.
    Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys 34(1), 1–47 (2002)CrossRefGoogle Scholar
  6. 6.
    Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)CrossRefMATHGoogle Scholar
  7. 7.
    Yang, Y., Liu, X.: A re-examination of text categorization methods. In: Proc. SIGIR 1999, the 22nd ACM Conference on Research and Development in Information Retrieval, Berkeley, USA, August 1999, pp. 42–49 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • David Lojo
    • 1
    • 2
  • David E. Losada
    • 3
  • Álvaro Barreiro
    • 1
  1. 1.IRLab. Dep. de ComputaciónUniversidade da CoruñaSpain
  2. 2.Servicio de InformáticaComplexo Hospitalario Universitario de SantiagoSantiago de CompostelaSpain
  3. 3.Grupo de Sistemas Inteligentes, Dep. de Electrónica y ComputaciónUniversidade de Santiago de CompostelaSpain

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