Clinical chemistry in higher dimensions: Machine-learning and enhanced prediction from routine clinical chemistry data
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Signor, Ben M
Lidbury, Brett A
Badrick, Tony
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Abstract
Big 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.
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Clinical Biochemistry
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49
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16-17
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© 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.
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Medical biochemistry and metabolomics
Clinical sciences
Science & Technology
Life Sciences & Biomedicine
Medical Laboratory Technology
Anaemia
Big data
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Richardson, 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