Detecting Interesting Exceptions from Medical Test Data with Visual Summarization

  • Authors:
  • Einoshin Suzuki;Takeshi Watanabe;Hideto Yokoi;Katsuhiko Takabayashi

  • Affiliations:
  • -;-;-;-

  • Venue:
  • ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
  • Year:
  • 2003

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Abstract

In this paper, we propose a method which visualizes irregularmulti-dimensional time-series data as a sequence ofprobabilistic prototypes for detecting exceptions from medicaltest data. Conventional visualization methods often requireiterative analysis and considerable skill thus are nottotally supported by a wide range of medical experts. OurPrototypeLines displays summarized information based ona probabilistic mixture model by using hue only thus is consideredto exhibit novelty. The effectiveness of the summarizationis pursued mainly through use of a novel informationcriterion. We report our endeavor with chronic hepatitisdata, especially discoveries of interesting exceptions bya non-expert and an untrained expert.