Empirical Bayesian data mining for discovering patterns in post-marketing drug safety
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting adverse events for patient safety research: a review of current methodologies
Journal of Biomedical Informatics - Patient safety
Analysis of breast feeding data using data mining methods
AusDM '06 Proceedings of the fifth Australasian conference on Data mining and analystics - Volume 61
Discovering debtor patterns of centrelink customers
AusDM '06 Proceedings of the fifth Australasian conference on Data mining and analystics - Volume 61
Journal of Biomedical Informatics
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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.