Medical document categorization using a priori knowledge

  • Authors:

  • Lukasz Itert;Włodzisław Duch;John Pestian

  • Affiliations:

  • Department of Biomedical Informatics, Children’s Hospital Research Foundation, Cincinnati, OH;Department of Informatics, Nicolaus Copernicus University, Toruń, Poland;Department of Biomedical Informatics, Children’s Hospital Research Foundation, Cincinnati, OH

  • Venue:
  • ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
  • Year:
  • 2005

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Abstract

A significant part of medical data remains stored as unstructured texts. Semantic search requires introduction of markup tags. Experts use their background knowledge to categorize new documents, and knowing category of these documents disambiguate words and acronyms. A model of document similarity that includes a priori knowledge and captures intuition of an expert, is introduced. It has only a few parameters that may be evaluated using linear programming techniques. This approach applied to categorization of medical discharge summaries provided simpler and much more accurate model than alternative text categorization approaches.