CIE-9-MC Code Classification with knn and SVM

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


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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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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