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Applications of machine learning in the chemical pathology laboratory
  1. Rivak Punchoo1,2,
  2. Sachin Bhoora2,
  3. Nelishia Pillay3
  1. 1 Tshwane Academic Division, National Health Laboratory Service, Pretoria, Gauteng, South Africa
  2. 2 Chemical Pathology, University of Pretoria Faculty of Health Sciences, Pretoria, Gauteng, South Africa
  3. 3 Computer Science, University of Pretoria Faculty of Engineering Built Environment and IT, Pretoria, Gauteng, South Africa
  1. Correspondence to Dr Rivak Punchoo, Tshwane Academic Division, National Health Laboratory Service, Pretoria 0001, South Africa; rivak.punchoo{at}up.ac.za

Abstract

Machine learning (ML) is an area of artificial intelligence that provides computer programmes with the capacity to autodidact and learn new skills from experience, without continued human programming. ML algorithms can analyse large data sets quickly and accurately, by supervised and unsupervised learning techniques, to provide classification and prediction value outputs. The application of ML to chemical pathology can potentially enhance efficiency at all phases of the laboratory’s total testing process. Our review will broadly discuss the theoretical foundation of ML in laboratory medicine. Furthermore, we will explore the current applications of ML to diverse chemical pathology laboratory processes, for example, clinical decision support, error detection in the preanalytical phase, and ML applications in gel-based image analysis and biomarker discovery. ML currently demonstrates exploratory applications in chemical pathology with promising advancements, which have the potential to improve all phases of the chemical pathology total testing pathway.

  • computer systems
  • chemistry
  • clinical
  • medical informatics computing
  • medical laboratory science
  • medical informatics

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Footnotes

  • Handling editor Tahir S Pillay.

  • Contributors All authors wrote the first, subsequent drafts and reviewed the final draft of the manuscript. SB prepared figures and table. All authors conceived the idea for the manuscript.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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