A novel delta check method for detecting laboratory errors

J. Sourati, D. Erdogmus, M. Akcakaya, S. C. Kazmierczak, T. K. Leen

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

Abstract

Investigating the variation of clinical measurements of patients over time is a common technique, known as delta check, for detecting laboratory errors. They are based on the expected biological variations and machine imprecision, where the latter varies for different concentrations of the analytes. Here, we present a novel delta check method in the form of composite thresholding, and provide its sufficient statistics by constructing the corresponding discriminant function, which enables us to use statistical and learning analysis tools. Using the scores obtained from such a discriminant function, we statistically study the performance of our algorithm on a labeled data set for the purpose of detecting lab errors.

Original languageEnglish (US)
Title of host publication2015 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2015
EditorsDeniz Erdogmus, Serdar Kozat, Jan Larsen, Murat Akcakaya
PublisherIEEE Computer Society
ISBN (Electronic)9781467374545
DOIs
StatePublished - Nov 10 2015
Event25th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2015 - Boston, United States
Duration: Sep 17 2015Sep 20 2015

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume2015-November
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Other

Other25th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2015
CountryUnited States
CityBoston
Period9/17/159/20/15

Keywords

  • Delta check
  • Sufficient statistics
  • lab error detection

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing

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  • Cite this

    Sourati, J., Erdogmus, D., Akcakaya, M., Kazmierczak, S. C., & Leen, T. K. (2015). A novel delta check method for detecting laboratory errors. In D. Erdogmus, S. Kozat, J. Larsen, & M. Akcakaya (Eds.), 2015 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2015 [7324343] (IEEE International Workshop on Machine Learning for Signal Processing, MLSP; Vol. 2015-November). IEEE Computer Society. https://doi.org/10.1109/MLSP.2015.7324343