Detecting interesting exceptions from medical test data with visual summarization

Einoshin Suzuki; Takeshi Watanabe; Hideto Yokoi; Katsuhiko Takabayashi

Detecting interesting exceptions from medical test data with visual summarization

Einoshin Suzuki, Takeshi Watanabe, Hideto Yokoi, Katsuhiko Takabayashi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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.

Original language	English
Title of host publication	Proceedings - 3rd IEEE International Conference on Data Mining, ICDM 2003
Pages	315-322
Number of pages	8
Publication status	Published - Dec 1 2003
Externally published	Yes
Event	3rd IEEE International Conference on Data Mining, ICDM '03 - Melbourne, FL, United States Duration: Nov 19 2003 → Nov 22 2003

Other

Other	3rd IEEE International Conference on Data Mining, ICDM '03
Country	United States
City	Melbourne, FL
Period	11/19/03 → 11/22/03

All Science Journal Classification (ASJC) codes

Engineering(all)

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Cite this

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title = "Detecting interesting exceptions from medical test data with visual summarization",

abstract = "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.",

author = "Einoshin Suzuki and Takeshi Watanabe and Hideto Yokoi and Katsuhiko Takabayashi",

year = "2003",

month = dec,

day = "1",

language = "English",

isbn = "0769519784",

pages = "315--322",

booktitle = "Proceedings - 3rd IEEE International Conference on Data Mining, ICDM 2003",

note = "3rd IEEE International Conference on Data Mining, ICDM '03 ; Conference date: 19-11-2003 Through 22-11-2003",

}

TY - GEN

T1 - Detecting interesting exceptions from medical test data with visual summarization

AU - Suzuki, Einoshin

AU - Watanabe, Takeshi

AU - Yokoi, Hideto

AU - Takabayashi, Katsuhiko

PY - 2003/12/1

Y1 - 2003/12/1

N2 - 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.

AB - 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.

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M3 - Conference contribution

AN - SCOPUS:24644453534

SN - 0769519784

SN - 9780769519784

SP - 315

EP - 322

BT - Proceedings - 3rd IEEE International Conference on Data Mining, ICDM 2003

T2 - 3rd IEEE International Conference on Data Mining, ICDM '03

Y2 - 19 November 2003 through 22 November 2003

ER -