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Data-driven dementia diagnosis record visualization system

Published: 14 August 2017 Publication History

Abstract

In this study, we propose 'Dementia Tracker' which is a visualization system with 21,094 dementia records for 8 years. This system makes it easy to understand complex dementia record data. In addition, the patient's own record is not only well-read, but also can be compared with people who have similar degree of dementia through the group filter function. Therefore, the current dementia situation can be understood more easily, and the future dementia can be predicted and prevented in advance.

References

[1]
Krzywinski, Martin, et al. Circos: an information aesthetic for comparative genomics. Genome research, 2009, 19.9: 1639--1645.
[2]
Choi, Seong Hye, et al. Driving in Patients with Dementia: A CREDOS (Clinical Research Center for Dementia of South Korea) Study. Dementia and Neurocognitive Disorders, 2014, 13.4: 83--88.
[3]
Bang, Sunjoo, et al. Quad-phased data mining modeling for dementia diagnosis. BMC medical informatics and decision making, 2017, 17.1: 60.

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  1. Data-driven dementia diagnosis record visualization system

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    VINCI '17: Proceedings of the 10th International Symposium on Visual Information Communication and Interaction
    August 2017
    158 pages
    ISBN:9781450352925
    DOI:10.1145/3105971
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

    Sponsors

    • KMUTT: King Mongkut's University of Technology Thonburi

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 14 August 2017

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    Author Tags

    1. data-driven
    2. dementia diagnosis
    3. visualization

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    • Poster

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    VINCI '17
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    • KMUTT

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    VINCI '17 Paper Acceptance Rate 12 of 27 submissions, 44%;
    Overall Acceptance Rate 71 of 193 submissions, 37%

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