Medical Document Categorization Using a Priori Knowledge

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.

Authors and Affiliations

1. Department of Biomedical Informatics, Children’s Hospital Research Foundation, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA

   Lukasz Itert & John Pestian

2. Department of Informatics, Nicolaus Copernicus University, Toruń, Poland

   Lukasz Itert & Włodzisław Duch

3. School of Computer Engineering, Nanyang Technological University, Singapore

   Włodzisław Duch

Editors and Affiliations

1. Department of Informatics, Nicolaus Copernicus University, Toruń, Poland

   Włodzisław Duch

2. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01–447, Warsaw, Poland

   Janusz Kacprzyk

3. Adaptive Informatics Research Centre, Helsinki University of Technology, P.O. Box 5400, 02015 HUT, Finland

   Erkki Oja

4. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

   Sławomir Zadrożny

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