Mining disease sequential risk patterns from nationwide clinical databases for early assessment of chronic obstructive pulmonary disease

Abstract

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.