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 language | English (US) |
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Title of host publication | Biochemical and Molecular Basis of Pediatric Disease |
Publisher | Elsevier |
Pages | 37-70 |
Number of pages | 34 |
ISBN (Electronic) | 9780128179628 |
DOIs | |
State | Published - 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