Signaling potential adverse drug reactions from administrative health databases

dc.contributor.authorJin, Huidong (Warren)
dc.contributor.authorChen, Jie
dc.contributor.authorHe, Hongxing
dc.contributor.authorKelman, Chris
dc.contributor.authorMcAullay, Damien
dc.contributor.authorO'Keefe, Christine
dc.date.accessioned2015-12-10T23:09:08Z
dc.date.issued2010
dc.date.updated2016-02-24T08:32:47Z
dc.description.abstractThe work is motivated by real-world applications of detecting Adverse Drug Reactions (ADRs) from administrative health databases. ADRs are a leading cause of hospitalization and death worldwide. Almost all current postmarket ADR signaling techniques are based on spontaneous ADR case reports, which suffer from serious underreporting and latency. However, administrative health data are widely and routinely collected. They, especially linked together, would contain evidence of all ADRs. To signal unexpected and infrequent patterns characteristic of ADRs, we propose a domain-driven knowledge representation Unexpected Temporal Association Rule (UTAR), its interestingness measure, unexlev, and a mining algorithm MUTARA (Mining UTARs given the Antecedent). We then establish an improved algorithm, HUNT, for highlighting infrequent and unexpected patterns by comparing their ranks based on unexlev with those based on traditional leverage. Various experimental results on real-world data substantiate that both MUTARA and HUNT can signal suspected ADRs while traditional association mining techniques cannot. HUNT can reliably shortlist statistically significantly more ADRs than MUTARA (p=0.00078). HUNT, e.g., not only shortlists the drug alendronate associated with esophagitis as MUTARA does, but also shortlists alendronate with diarrhoea and vomiting for older (age ≥ 60) females. We also discuss signaling ADRs systematically by using HUNT.
dc.identifier.issn1372-1387
dc.identifier.urihttp://hdl.handle.net/1885/63352
dc.publisherIEEE Computer Society
dc.sourceIEEE Transactions on Knowledge and Data Engineering
dc.subjectKeywords: Adverse drug reactions; Alendronate; Association mining; Case reports; Esophagitis; Health data; Improved algorithm; Interestingness measures; Mining algorithms; Mining methods and algorithms; Real world data; Real-world application; Signaling techniques; Association rules; Medicine and science; Mining methods and algorithms
dc.titleSignaling potential adverse drug reactions from administrative health databases
dc.typeJournal article
local.bibliographicCitation.issue6
local.bibliographicCitation.lastpage853
local.bibliographicCitation.startpage839
local.contributor.affiliationJin, Huidong (Warren), College of Engineering and Computer Science, ANU
local.contributor.affiliationChen, Jie, CSIRO Mathematical and Information Sciences
local.contributor.affiliationHe, Hongxing, CSIRO Mathematical and Information Sciences
local.contributor.affiliationKelman, Chris, College of Medicine, Biology and Environment, ANU
local.contributor.affiliationMcAullay, Damien, CSIRO Mathematical and Information Sciences
local.contributor.affiliationO'Keefe, Christine, CSIRO Division of Mathematical and Information Sciences
local.contributor.authoremailu1817131@anu.edu.au
local.contributor.authoruidJin, Huidong (Warren), u1817131
local.contributor.authoruidKelman, Chris, u3883220
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.identifier.absfor111799 - Public Health and Health Services not elsewhere classified
local.identifier.absfor080109 - Pattern Recognition and Data Mining
local.identifier.ariespublicationf2965xPUB796
local.identifier.citationvolume22
local.identifier.doi10.1109/TKDE.2009.212
local.identifier.scopusID2-s2.0-77951765455
local.identifier.thomsonID000276801300008
local.identifier.uidSubmittedByf2965
local.type.statusPublished Version

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