Mining risk patterns in medical data

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. Categories and Subject Descriptors H.2.8 [Database Management]: Database Applications--Data mining; J.3 [Life and Medical Sciences]: Health General Terms Algorithm, performance Keywords Relative risk, rule, optimal risk pattern set, medical application

• Type: Conference
• Year: 2005
• Where: KDD
• Authors: Jiuyong Li, Ada Wai-Chee Fu, Hongxing He, Jie Chen, Huidong Jin, Damien McAullay, Graham J. Williams, Ross Sparks, Chris Kelman
• Keywords: Data Mining, KDD 2005, Mining Optimal Risk, Mining Risk Patterns, Optimal Risk Pattern