Representing Association Classification Rules Mined from Health Data
dc.contributor.author | Chen, Jie | |
dc.contributor.author | He, Hongxing | |
dc.contributor.author | Jin, Huidong | |
dc.contributor.author | McAullay, Damien | |
dc.contributor.author | Williams, Graham | |
dc.contributor.author | Sparks, Ross | |
dc.contributor.author | Kelman, Chris | |
dc.date.accessioned | 2015-12-13T22:52:18Z | |
dc.date.available | 2015-12-13T22:52:18Z | |
dc.date.issued | 2005 | |
dc.date.updated | 2015-12-11T10:50:20Z | |
dc.description.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. | |
dc.identifier.isbn | 3540288961 | |
dc.identifier.uri | http://hdl.handle.net/1885/81506 | |
dc.publisher | Springer | |
dc.relation.ispartof | Knowledge-Based Intelligent Information and Engineering Systems 9th International Conference, KES 2005, Melbourne, Australia, September 14-16, 2005, Proceedings, Part III | |
dc.relation.isversionof | 1st Edition | |
dc.subject | Keywords: Algorithms; Classification (of information); Drug products; Probability; Risk assessment; Trees (mathematics); Association classification algorithm; Drug reactions; Health data; Medical practitioners; Data mining | |
dc.title | Representing Association Classification Rules Mined from Health Data | |
dc.type | Book chapter | |
local.bibliographicCitation.lastpage | 1231 | |
local.bibliographicCitation.placeofpublication | Germany | |
local.bibliographicCitation.startpage | 1225 | |
local.contributor.affiliation | Chen, Jie, CSIRO Mathematical and Information Sciences | |
local.contributor.affiliation | He, Hongxing, CSIRO Mathematical and Information Sciences | |
local.contributor.affiliation | Jin, Huidong, CSIRO Division of Mathematical and Information Sciences | |
local.contributor.affiliation | McAullay, Damien, CSIRO Mathematical and Information Sciences | |
local.contributor.affiliation | Williams, Graham, CSIRO Mathematical and Information Sciences | |
local.contributor.affiliation | Sparks, Ross, CSIRO Mathematical and Information Sciences | |
local.contributor.affiliation | Kelman, Chris, College of Medicine, Biology and Environment, ANU | |
local.contributor.authoremail | repository.admin@anu.edu.au | |
local.contributor.authoruid | Kelman, Chris, u3883220 | |
local.description.notes | Imported from ARIES | |
local.description.refereed | Yes | |
local.identifier.absfor | 111711 - Health Information Systems (incl. Surveillance) | |
local.identifier.absfor | 080608 - Information Systems Development Methodologies | |
local.identifier.ariespublication | MigratedxPub9788 | |
local.identifier.scopusID | 2-s2.0-33745292794 | |
local.identifier.uidSubmittedBy | Migrated | |
local.type.status | Published Version |