Browsing by Author "Chiang, Kuo-Hwa"

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Mining Disease Sequential Risk Patterns from Nationwide Clinical Databases for Early Assessment of Chronic Obstructive Pulmonary Disease
(2016)
Cheng, Yi-Ting
;
Lin, Yu-Feng
;
Chiang, Kuo-Hwa
;
Tseng, Vincent S.
;
資訊工程學系
Chronic diseases may cause heavy burden on health care resources and disturb the quality of life. Chronic Obstructive Pulmonary Disease (COPD) is an important chronic disease, which takes a long period of time to progress and hard to detect in early stage. In this work, we propose a novel approach for early assessment on COPD by mining COPD-related sequential risk patterns from diagnostic clinical records using sequential rule mining and classification techniques. Through experimental evaluation on a large-scale nationwide clinical database in Taiwan, our approach is shown to be not only capable of deriving many sequential risk patterns, but also reliable in prediction results. Moreover, the discovered sequential risk patterns may provide potential clues for physicians to derive novel markers for early detection on COPD. To our best knowledge, this is the first work that addresses the important issue of early assessment on COPD through mining sequential risk patterns from large-scale clinical databases.
Mining Sequential Risk Patterns From Large-Scale Clinical Databases for Early Assessment of Chronic Diseases: A Case Study on Chronic Obstructive Pulmonary Disease
(2017-03-01)
Cheng, Yi-Ting
;
Lin, Yu-Feng
;
Chiang, Kuo-Hwa
;
Tseng, Vincent S.
;
資訊工程學系
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.