Machine learning and big data in pediatric laboratory medicine

Shannon Haymond, Randall K. Julian, Emily L. Gill, Stephen R. Master

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Scopus citations

Abstract

Clinical laboratories generate a large number of test results, creating opportunities for improved data management and the use of analytics. Aggregate analyses of these data have potential diagnostic value but require labs to utilize computational tools for the analysis of high-dimensional data. Machine learning can be used to aid decision-making, whether for clinical or operational purposes, using a variety of algorithms to analyze complex data sets and make reliable predictions. This chapter discusses key concepts related to big data and its application to pediatric laboratory medicine. Machine learning workflows, concepts, common algorithms, and related infrastructure requirements are also covered.

Original languageEnglish (US)
Title of host publicationBiochemical and Molecular Basis of Pediatric Disease
PublisherElsevier
Pages37-70
Number of pages34
ISBN (Electronic)9780128179628
DOIs
StatePublished - Jan 1 2021

Keywords

  • Artificial intelligence
  • Big data
  • Laboratory medicine
  • Machine learning
  • Pediatrics
  • Regulation

ASJC Scopus subject areas

  • General Agricultural and Biological Sciences
  • General Biochemistry, Genetics and Molecular Biology

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