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dc.contributor.authorDemir, Eren
dc.date.accessioned2012-05-17T14:56:50Z
dc.date.available2012-05-17T14:56:50Z
dc.date.issued2011
dc.identifier.citationDemir , E 2011 , Data preparation for clinical data mining to identify patients at risk of readmission . in Procs of 2010 IEEE 23rd Int Symposium on Computer Based Medical Systems : CBMS . IEEE , pp. 184-189 , 23rd Int Symposium on Computer-Based Medical Systems , Perth , Australia , 12/10/10 . https://doi.org/10.1109/CBMS.2010.6042638
dc.identifier.citationconference
dc.identifier.isbn978-1-4244-9167-4
dc.identifier.otherPURE: 682664
dc.identifier.otherPURE UUID: c2ea2574-df6b-495e-a0a3-6b213a2dcee9
dc.identifier.otherScopus: 80055092049
dc.identifier.urihttp://hdl.handle.net/2299/8527
dc.description.abstractSteadily rising numbers of emergency (unplanned) inpatient admissions have been the major source of pressure on the NHS over the past twenty years. There is currently still a strong need for a consistent predictive tool and an automation of the development of re-admission risk profiles, in particular, one that addresses both data preparation and predictive modelling. This paper proposes a data preparation framework for transforming raw transactional clinical data to well-formed data sets so that data mining can be applied. In this framework, rules are created according to the statistical characteristics of the data, the metadata that characterises the host information systems and medical knowledge. These rules can be used for data pre-processing, attribute selection and data transformation in order to generate appropriately prepared data sets. The proposed data preparation framework incorporates automatic methods with heuristic pre-processing treatments for the potential challenges within a large-scaled development and its applicability is not limited to clinical data.en
dc.language.isoeng
dc.publisherIEEE
dc.relation.ispartofProcs of 2010 IEEE 23rd Int Symposium on Computer Based Medical Systems
dc.titleData preparation for clinical data mining to identify patients at risk of readmissionen
dc.contributor.institutionDepartment of Marketing and Enterprise
dc.contributor.institutionSocial Sciences, Arts & Humanities Research Institute
dc.contributor.institutionStatistical Services Consulting Unit
dc.contributor.institutionHealth Services and Medicine
dc.contributor.institutionCentre for Research on Management, Economy and Society
rioxxterms.versionofrecordhttps://doi.org/10.1109/CBMS.2010.6042638
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue
herts.rights.accesstyperestrictedAccess


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