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 language | English |
---|---|
Pages | 770-775 |
Number of pages | 6 |
DOIs | |
Publication status | Published - 2005 |
Externally published | Yes |
Event | KDD-2005: 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Chicago, IL, United States Duration: 21 Aug 2005 → 24 Aug 2005 |
Conference
Conference | KDD-2005: 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining |
---|---|
Country/Territory | United States |
City | Chicago, IL |
Period | 21/08/05 → 24/08/05 |