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

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Date

Authors

Signor, B.M.
Lidbury, Brett
Badrick, T.
Richardson, Alice

Journal Title

Journal ISSN

Volume Title

Publisher

Elsevier Inc.

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. � 2016 The Canadian Society of Clinical Chemists

Description

Citation

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Book Title

Clinical Biochemistry

Entity type

Access Statement

Open Access

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Restricted until

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