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

13 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
Externally publishedYes

Keywords

  • Anaemia
  • Big data
  • Bilirubin
  • Biomarkers
  • Hepatitis
  • Liver function tests
  • Misconceptions
  • Predictive modelling
  • Statistics

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