Mining Sequential Risk Patterns From Large-Scale Clinical Databases for Early Assessment of Chronic Diseases: A Case Study on Chronic Obstructive Pulmonary Disease

doi:10.1109/JBHI.2017.2657802

CNU IR > The Centers > The Center For General Education > Periodical Articles > Item 310902800/31659

Please use this identifier to cite or link to this item: https://ir.cnu.edu.tw/handle/310902800/31659

Title:	Mining Sequential Risk Patterns From Large-Scale Clinical Databases for Early Assessment of Chronic Diseases: A Case Study on Chronic Obstructive Pulmonary Disease
Authors:	Cheng, Yi-Ting Lin, Yu-Feng Chiang, Kuo-Hwa Tseng, Vincent S.
Contributors:	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
Keywords:	Data mining disease risk assessment early prediction electronic medical records sequential patterns
Date:	2017-03
Issue Date:	2018-11-30 15:51:53 (UTC+8)
Publisher:	Ieee-Inst Electrical Electronics Engineers Inc
Abstract:	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.
Relation:	Ieee Journal of Biomedical and Health Informatics, v.21, n.2, pp.303-311
Appears in Collections:	[The Center For General Education] Periodical Articles

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	1232	View/Open

Loading...