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Discovering patterns of medical practice in large administrative health databases

Semenova, Tatiana

Description

Health databases are characterised by large number of records, large number of attributes and mild density. This encourages data miners to use methodologies that are more sensitive to health industry specifics. For conceptual mining, the classic pattern-growth methods are found limited due to their great resource consumption. As an alternative, we propose a technique that uses some of the properties of graphs. Such a technique delivers as complete and compact knowledge about the data as the...[Show more]

dc.contributor.authorSemenova, Tatiana
dc.date.accessioned2015-12-13T22:50:36Z
dc.identifier.issn0169-023X
dc.identifier.urihttp://hdl.handle.net/1885/80859
dc.description.abstractHealth databases are characterised by large number of records, large number of attributes and mild density. This encourages data miners to use methodologies that are more sensitive to health industry specifics. For conceptual mining, the classic pattern-growth methods are found limited due to their great resource consumption. As an alternative, we propose a technique that uses some of the properties of graphs. Such a technique delivers as complete and compact knowledge about the data as the pattern-growth techniques, but is found to be more efficient.
dc.publisherElsevier
dc.sourceData and Knowledge Engineering
dc.subjectKeywords: Compact knowledge; Complexity reduction; Galois lattices; Computational complexity; Data mining; Health care; Knowledge acquisition; Medical applications; Patient treatment; Database systems Complexity reduction; Data mining; Galois lattices; Health care
dc.titleDiscovering patterns of medical practice in large administrative health databases
dc.typeJournal article
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.citationvolume51
dc.date.issued2004
local.identifier.absfor080603 - Conceptual Modelling
local.identifier.ariespublicationMigratedxPub9159
local.type.statusPublished Version
local.contributor.affiliationSemenova, Tatiana, College of Engineering and Computer Science, ANU
local.description.embargo2037-12-31
local.bibliographicCitation.startpage149
local.bibliographicCitation.lastpage160
local.identifier.doi10.1016/j.datak.2004.02.001
dc.date.updated2015-12-11T10:41:32Z
local.identifier.scopusID2-s2.0-4344657843
CollectionsANU Research Publications

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