Detecting Interesting Exceptions from Medical Test Data with Visual Summarization

I
ICDM
2003
Third IEEE International Conference on Data Mining (ICDM'03)
Abstract - Detecting Interesting Exceptions from Medical Test Data with Visual Summarization

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Third IEEE International Conference on Data Mining (ICDM'03)

Melbourne, Florida

November 19-November 22

ISBN: 0-7695-1978-4

	ASCII Text	x
	Einoshin Suzuki, Takeshi Watanabe, Hideto Yokoi, Katsuhiko Takabayashi, "Detecting Interesting Exceptions from Medical Test Data with Visual Summarization," Data Mining, IEEE International Conference on, pp. 315, Third IEEE International Conference on Data Mining (ICDM'03), 2003.

	BibTex	x
	@article{ 10.1109/ICDM.2003.1250935, author = {Einoshin Suzuki and Takeshi Watanabe and Hideto Yokoi and Katsuhiko Takabayashi}, title = {Detecting Interesting Exceptions from Medical Test Data with Visual Summarization}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2003}, isbn = {0-7695-1978-4}, pages = {315}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2003.1250935}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Mining, IEEE International Conference on TI - Detecting Interesting Exceptions from Medical Test Data with Visual Summarization SN - 0-7695-1978-4 SP EP A1 - Einoshin Suzuki, A1 - Takeshi Watanabe, A1 - Hideto Yokoi, A1 - Katsuhiko Takabayashi, PY - 2003 KW - null VL - 0 JA - Data Mining, IEEE International Conference on ER -

Einoshin Suzuki, Yokohama National University, Japan

Takeshi Watanabe, Yokohama National University, Japan

Hideto Yokoi, Chiba University Hospital, Japan

Katsuhiko Takabayashi, Chiba University Hospital, Japan

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2003.1250935

In this paper, we propose a method which visualizes irregular multi-dimensional time-series data as a sequence of probabilistic prototypes for detecting exceptions from medical test data. Conventional visualization methods often require iterative analysis and considerable skill thus are not totally supported by a wide range of medical experts. Our PrototypeLines displays summarized information based on a probabilistic mixture model by using hue only thus is considered to exhibit novelty. The effectiveness of the summarization is pursued mainly through use of a novel information criterion. We report our endeavor with chronic hepatitis data, especially discoveries of interesting exceptions by a non-expert and an untrained expert.

Citation:

Einoshin Suzuki, Takeshi Watanabe, Hideto Yokoi, Katsuhiko Takabayashi, "Detecting Interesting Exceptions from Medical Test Data with Visual Summarization," icdm, pp.315, Third IEEE International Conference on Data Mining (ICDM'03), 2003

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