Representing Association Classification Rules Mined from Health Data

dc.contributor.authorChen, Jie
dc.contributor.authorHe, Hongxing
dc.contributor.authorJin, Huidong
dc.contributor.authorMcAullay, Damien
dc.contributor.authorWilliams, Graham
dc.contributor.authorSparks, Ross
dc.contributor.authorKelman, Chris
dc.date.accessioned2015-12-13T22:52:18Z
dc.date.available2015-12-13T22:52:18Z
dc.date.issued2005
dc.date.updated2015-12-11T10:50:20Z
dc.description.abstractAn 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.isbn3540288961
dc.identifier.urihttp://hdl.handle.net/1885/81506
dc.publisherSpringer
dc.relation.ispartofKnowledge-Based Intelligent Information and Engineering Systems 9th International Conference, KES 2005, Melbourne, Australia, September 14-16, 2005, Proceedings, Part III
dc.relation.isversionof1st Edition
dc.subjectKeywords: Algorithms; Classification (of information); Drug products; Probability; Risk assessment; Trees (mathematics); Association classification algorithm; Drug reactions; Health data; Medical practitioners; Data mining
dc.titleRepresenting Association Classification Rules Mined from Health Data
dc.typeBook chapter
local.bibliographicCitation.lastpage1231
local.bibliographicCitation.placeofpublicationGermany
local.bibliographicCitation.startpage1225
local.contributor.affiliationChen, Jie, CSIRO Mathematical and Information Sciences
local.contributor.affiliationHe, Hongxing, CSIRO Mathematical and Information Sciences
local.contributor.affiliationJin, Huidong, CSIRO Division of Mathematical and Information Sciences
local.contributor.affiliationMcAullay, Damien, CSIRO Mathematical and Information Sciences
local.contributor.affiliationWilliams, Graham, CSIRO Mathematical and Information Sciences
local.contributor.affiliationSparks, Ross, CSIRO Mathematical and Information Sciences
local.contributor.affiliationKelman, Chris, College of Medicine, Biology and Environment, ANU
local.contributor.authoremailrepository.admin@anu.edu.au
local.contributor.authoruidKelman, Chris, u3883220
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor111711 - Health Information Systems (incl. Surveillance)
local.identifier.absfor080608 - Information Systems Development Methodologies
local.identifier.ariespublicationMigratedxPub9788
local.identifier.scopusID2-s2.0-33745292794
local.identifier.uidSubmittedByMigrated
local.type.statusPublished Version

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