@inproceedings{d027a41265c249b7b9731903744e786a,
title = "Representing association classification rules mined from health data",
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.",
author = "Jie Chen and Hongxing He and Jiuyong Li and Huidong Jin and Damien McAullay and Graham Williams and Ross Sparks and Chris Kelman",
year = "2005",
doi = "10.1007/11553939_170",
language = "English",
isbn = "3540288961",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "1225--1231",
booktitle = "Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings",
address = "Germany",
note = "9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005 ; Conference date: 14-09-2005 Through 16-09-2005",
}