Mining Diagnostic Taxonomy Using Interval-Based Similarity from Clinical Databases

  • Shusaku Tsumoto
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3131)


Experts’ reasoning in which selects the final diagnosis from many candidates consists of hierarchical differential diagnosis. In other words, candidates gives a sophisticated hiearchical taxonomy, usally described as a tree. In this paper, the characteristics of experts’ rules are closely examined from the viewpoint of hiearchical decision steps and and a new approach to rule mining with extraction of diagnostic taxonomy from medical datasets is introduced. The key elements of this approach are calculation of the characterization set of each decision attribute (a given class) and the similarities between characterization sets. From the relations between similarities, tree-based taxonomy is obtained, which includes enough information for diagnostic rules.


Medical Expert Clinical Database Decision Attribute Target Concept Rule Induction 
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 2004

Authors and Affiliations

  • Shusaku Tsumoto
    • 1
  1. 1.Department of Medical InformaticsShimane University, School of MedicineIzumo City, ShimaneJapan

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