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Discovering Associations in Clinical Data: Application to Search for Prognostic Factors in Hodgkin’s Disease

  • N. Durand
  • B. Crémilleux
  • M. Henry-Amar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2101)

Abstract

The production of suitable clusters to help physicians explore data and take decisions is a hard task. This paper addresses this question and proposes a new method to define clusters of patients which takes advantage of the power of association rules method. We present different notions of association and we specify the notion of frequent almost closed itemset which is the most appropriate for applications in the medical area. Applied to Hodgkin’s disease to help establish prognostic groups, the first results bring out some parameters for which classical statistic methods confirm that they are interesting.

Keywords

Association Rule Frequent Itemsets Medical Area Prognostic Group White Blood Count 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • N. Durand
    • 1
  • B. Crémilleux
    • 1
  • M. Henry-Amar
    • 2
  1. 1.GREYC, CNRS - UMR 6072Université de CaenCaen CédexFrance
  2. 2.GRECAN, EA 1772Centre François BaclesseCaen Cédex 5France

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