Representing Association Classification Rules Mined from Health Data
Date
2005
Authors
Chen, Jie
He, Hongxing
Jin, Huidong
McAullay, Damien
Williams, Graham
Sparks, Ross
Kelman, Chris
Journal Title
Journal ISSN
Volume Title
Publisher
Springer
Abstract
An association classification algorithm has been developed to explore adverse drug reactions in a large medical transaction dataset with unbalanced classes. Rules discovered can be used to alert medical practitioners when prescribing drugs, to certain categories of patients, to potential adverse effects. We assess the rules using survival charts and propose two kinds of probability trees to present them. Both of them represent the risk of given adverse drug reaction for certain categories of patients in terms of risk ratios, which are familiar to medical practitioners. The first approach shows risk ratios when all rule conditions apply. The second presents the risk associated with a single risk factor with other parts of the rule identifying the cohort of the patient subpopulation. Thus, the probability trees can present clearly the risk of specific adverse drug reactions to prescribers.
Description
Keywords
Keywords: Algorithms; Classification (of information); Drug products; Probability; Risk assessment; Trees (mathematics); Association classification algorithm; Drug reactions; Health data; Medical practitioners; Data mining
Citation
Collections
Source
Type
Book chapter
Book Title
Knowledge-Based Intelligent Information and Engineering Systems 9th International Conference, KES 2005, Melbourne, Australia, September 14-16, 2005, Proceedings, Part III