Mining risk patterns in medical data

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

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

64 Citations (Scopus)

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.

Original languageEnglish
Pages770-775
Number of pages6
DOIs
Publication statusPublished - 2005
Externally publishedYes
EventKDD-2005: 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Chicago, IL, United States
Duration: 21 Aug 200524 Aug 2005

Conference

ConferenceKDD-2005: 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Country/TerritoryUnited States
CityChicago, IL
Period21/08/0524/08/05

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