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Review: Clinical chemistry in higher dimensions: Machine-learning and enhanced prediction from routine clinical chemistry data

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dc.contributor.authorSignor, B.M.en_AU
dc.contributor.authorLidbury, Bretten_AU
dc.contributor.authorBadrick, T.en_AU
dc.contributor.authorRichardson, Alice
dc.date.accessioned2017-12-14T06:51:30Z
dc.date.available2017-12-14T06:51:30Z
dc.date.issued2016en_AU
dc.description.abstractBig Data is having an impact on many areas of research, not the least of which is biomedical science. In this review paper, big data and machine learning are defined in terms accessible to the clinical chemistry community. Seven myths associated with machine learning and big data are then presented, with the aim of managing expectation of machine learning amongst clinical chemists. The myths are illustrated with four examples investigating the relationship between biomarkers in liver function tests, enhanced laboratory prediction of hepatitis virus infection, the relationship between bilirubin and white cell count, and the relationship between red cell distribution width and laboratory prediction of anaemia. � 2016 The Canadian Society of Clinical Chemistsen_AU
dc.format.extent7 pagesen_AU
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.urihttp://hdl.handle.net/1885/138141
dc.language.isoen_AUen_AU
dc.publisherElsevier Inc.en_AU
dc.relation.ispartofClinical Biochemistryen_AU
dc.subjectAnaemiaen_AU
dc.subjectBig dataen_AU
dc.subjectBilirubinen_AU
dc.subjectBiomarkersen_AU
dc.subjectHepatitisen_AU
dc.subjectLiver function testsen_AU
dc.subjectMisconceptionsen_AU
dc.subjectPredictive modellingen_AU
dc.subjectStatisticsen_AU
dc.titleReview: Clinical chemistry in higher dimensions: Machine-learning and enhanced prediction from routine clinical chemistry dataen_AU
dc.typeJournal articleen_AU
dcterms.accessRightsOpen Accessen_AU
local.bibliographicCitation.issue16-17en_AU
local.bibliographicCitation.lastpage1220en_AU
local.bibliographicCitation.startpage1213en_AU
local.contributor.affiliationRichardson, A., National Centre for Epidemiology & Population Health, Australian National University, 62 Mills Rd, Acton, ACT, Australiaen_AU
local.contributor.affiliationSignor, B.M., Pattern Recognition and Pathology, John Curtin School of Medical Research, Australian National University, 62 Mills Rd, Acton, ACT, Australiaen_AU
local.contributor.affiliationLidbury, B.A., Pattern Recognition and Pathology, John Curtin School of Medical Research, Australian National University, 62 Mills Rd, Acton, ACT, Australiaen_AU
local.contributor.affiliationBadrick, T., Pattern Recognition and Pathology, John Curtin School of Medical Research, Australian National University, 62 Mills Rd, Acton, ACT, Australia, RCPAQAP, Suite 201/8 Herbert Street, St Leonards, NSW, Australiaen_AU
local.identifier.citationvolume49en_AU
local.identifier.doi10.1016/j.clinbiochem.2016.07.013en_AU
local.identifier.scopusID2-s2.0-84979692017en_AU
local.type.statusPublished versionen_AU

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