Representing association classification rules mined from health data
Date
2005
Authors
Chen, J.
He, H.
Li, J.
Jin, H.
McAullay, D.
Williams, G.
Sparks, R.A.
Kelman, C.
Editors
Khosla, R.
Howlett, R.J.
Jain, L.C.
Howlett, R.J.
Jain, L.C.
Advisors
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Conference paper
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2005 / Khosla, R., Howlett, R.J., Jain, L.C. (ed./s), vol.3683 LNAI, pp.1225-1231
Statement of Responsibility
Conference Name
9th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (14 Sep 2005 - 16 Sep 2005 : Melbourne)
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.
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Copyright 2005 Springer-Verlag Berlin Heidelberg