title: Clinical chemistry in higher dimensions: Machine-learning and enhanced prediction from routine clinical chemistry data.
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.
authors.affiliation: National Centre for Epidemiology & Population Health, Australian National University, 62 Mills Rd, Acton, ACT 2601, Australia.
authors.forename: Alice
authors.initials: A
authors.lastname: Richardson
authors.affiliation: Pattern Recognition and Pathology, John Curtin School of Medical Research, Australian National University, 62 Mills Rd, Acton, ACT 2601, Australia.
authors.forename: Ben M
authors.initials: BM
authors.lastname: Signor
authors.affiliation: Pattern Recognition and Pathology, John Curtin School of Medical Research, Australian National University, 62 Mills Rd, Acton, ACT 2601, Australia.
authors.forename: Brett A
authors.initials: BA
authors.lastname: Lidbury
authors.affiliation: Pattern Recognition and Pathology, John Curtin School of Medical Research, Australian National University, 62 Mills Rd, Acton, ACT 2601, Australia; RCPAQAP, Suite 201/8 Herbert Street, St Leonards, NSW 2065, Australia. Electronic address: tony.badrick@rcpaqap.com.au.
authors.forename: Tony
authors.initials: T
authors.lastname: Badrick
mesh_terms: D019963:Clinical Chemistry Tests; D003627:Data Interpretation, Statistical; D000069550:Machine Learning
keywords: Anaemia; Big data; Bilirubin; Biomarkers; Hepatitis; Liver function tests; Misconceptions; Predictive modelling; Statistics