CIE-9-MC Code Classification with knn and SVM
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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Publication: Congress
1624015003718
June 18, 2021
/research/publications/cie-9-mc-code-classification-with-knn-and-svm
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. - David Lojo, David E. Losada, Alvaro Barreiro - 10.1007/978-3-642-02267-8_53
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