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

Alice Richardson, Ben M. Signor, Brett A. Lidbury, Tony Badrick*

*Corresponding author for this work

    Research output: Contribution to journalReview articlepeer-review

    37 Citations (Scopus)

    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.

    Original languageEnglish
    Pages (from-to)1213-1220
    Number of pages8
    JournalClinical Biochemistry
    Volume49
    Issue number16-17
    DOIs
    Publication statusPublished - 1 Nov 2016

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