Mining risk patterns in medical data

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Jiuyong Li, Ada Wai Chee Fu, Hongxing He, Jie Chen, Huidong Jin, Damien McAullay, Graham Williams, Ross Sparks, Chris Kelman

Published: 01 Jan 2005, Last Modified: 15 Jan 2026KDD-2005: 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, United States, 21/08/05EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: In this paper, we discuss a problem of finding risk patterns in medical data, We define risk patterns by a statistical metric, relative risk, which has been widely used in epidemiological research. We characterise the problem of mining risk patterns as an optimal rule discovery problem. We study an anti-monotone property for mining optimal risk pattern sets and present an algorithm to make use of the property in risk pattern discovery. The method has been applied to a real world data set to find patterns associated with an allergic event for ACE inhibitors. The algorithm has generated some useful results for medical researchers.