English  |  正體中文  |  简体中文  |  Items with full text/Total items : 17776/20117 (88%)
Visitors : 11008119      Online Users : 567
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://ir.cnu.edu.tw/handle/310902800/31659

    標題: Mining Sequential Risk Patterns From Large-Scale Clinical Databases for Early Assessment of Chronic Diseases: A Case Study on Chronic Obstructive Pulmonary Disease
    作者: Cheng, Yi-Ting
    Lin, Yu-Feng
    Chiang, Kuo-Hwa
    Tseng, Vincent S.
    貢獻者: Natl Cheng Kung Univ, Inst Med Informat
    Natl Cheng Kung Univ, Dept Comp Sci
    Chi Mei Med Ctr, Div Chest Med, Dept Internal Med
    Chia Nan Univ Pharm & Sci
    Natl Chao Tung Univ, Dept Comp Sci
    關鍵字: Data mining
    disease risk assessment
    early prediction
    electronic medical records
    sequential patterns
    日期: 2017-03
    上傳時間: 2018-11-30 15:51:53 (UTC+8)
    出版者: Ieee-Inst Electrical Electronics Engineers Inc
    摘要: Chronic diseases have been among the major concerns in medical fields since they may cause a heavy burden on healthcare resources and disturb the quality of life. In this paper, we propose a novel framework for early assessment on chronic diseases by mining sequential risk patterns with time interval information from diagnostic clinical records using sequential rules mining, and classification modeling techniques. With a complete workflow, the proposed framework consists of four phases namely data preprocessing, risk pattern mining, classification modeling, and post analysis. For empiricasl evaluation, we demonstrate the effectiveness of our proposed framework with a case study on early assessment of COPD. Through experimental evaluation on a large-scale nationwide clinical database in Taiwan, our approach can not only derive rich sequential risk patterns but also extract novel patterns with valuable insights for further medical investigation such as discovering novel markers and better treatments. To the best of our knowledge, this is the first work addressing the issue of mining sequential risk patterns with time-intervals as well as classification models for early assessment of chronic diseases.
    關聯: Ieee Journal of Biomedical and Health Informatics, v.21, n.2, pp.303-311
    Appears in Collections:[通識教育中心] 期刊論文

    Files in This Item:

    File Description SizeFormat

    All items in CNU IR are protected by copyright, with all rights reserved.

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback