Statistical error detection for clinical laboratory tests

Todd K. Leen, Deniz Erdogmus, Steven Kazmierczak

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

Errors in clinical laboratory tests lead to increased costs and patient risks. Such errors are relatively rare, affecting 0.5% of samples. Existing techniques for detecting errors have either far too low sensitivity or specificity to be useful. This preliminary study develops statistical sample selection criteria that capture faults upwards of fifty times more efficiently than expected from random sampling. Although this is only the first step towards an integrated discriminant system for reliable detection of laboratory errors, the statistical detection scheme demonstrated here outperforms existing methods.

Original languageEnglish (US)
Title of host publication2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012
Pages2720-2723
Number of pages4
DOIs
StatePublished - 2012
Event34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012 - San Diego, CA, United States
Duration: Aug 28 2012Sep 1 2012

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Other

Other34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012
Country/TerritoryUnited States
CitySan Diego, CA
Period8/28/129/1/12

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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