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dc.contributor.authorRichardson, Alice
dc.contributor.authorSignor, Ben M
dc.contributor.authorLidbury, Brett A
dc.contributor.authorBadrick, Tony
dc.date.accessioned2020-02-17T01:27:05Z
dc.date.available2020-02-17T01:27:05Z
dc.date.issued2016
dc.identifier.issn0009-9120
dc.identifier.doi10.1016/j.clinbiochem.2016.07.013
dc.identifier.urihttp://hdl.handle.net/10072/391551
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.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofpagefrom1213
dc.relation.ispartofpageto1220
dc.relation.ispartofissue16-17
dc.relation.ispartofjournalClinical Biochemistry
dc.relation.ispartofvolume49
dc.subject.fieldofresearchMedical Biochemistry and Metabolomics
dc.subject.fieldofresearchClinical Sciences
dc.subject.fieldofresearchcode1101
dc.subject.fieldofresearchcode1103
dc.subject.keywordsScience & Technology
dc.subject.keywordsLife Sciences & Biomedicine
dc.subject.keywordsMedical Laboratory Technology
dc.subject.keywordsAnaemia
dc.subject.keywordsBig data
dc.titleClinical chemistry in higher dimensions: Machine-learning and enhanced prediction from routine clinical chemistry data
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationRichardson, A; Signor, BM; Lidbury, BA; Badrick, T, Clinical chemistry in higher dimensions: Machine-learning and enhanced prediction from routine clinical chemistry data, Clinical Biochemistry 2016, 49 (16-17), pp. 1213-1220
dcterms.dateAccepted2016-07-20
dcterms.licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated2020-02-17T01:24:46Z
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2016 The Canadian Society of Clinical Chemists. Published by Elsevier Ltd. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorBadrick, Tony C.


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