@inproceedings{7c56645951de43e5bef99e2333ee7dea,
title = "A novel delta check method for detecting laboratory errors",
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.",
keywords = "Delta check, Sufficient statistics, lab error detection",
author = "J. Sourati and D. Erdogmus and M. Akcakaya and Kazmierczak, {S. C.} and Leen, {T. K.}",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 25th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2015 ; Conference date: 17-09-2015 Through 20-09-2015",
year = "2015",
month = nov,
day = "10",
doi = "10.1109/MLSP.2015.7324343",
language = "English (US)",
series = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",
publisher = "IEEE Computer Society",
editor = "Deniz Erdogmus and Serdar Kozat and Jan Larsen and Murat Akcakaya",
booktitle = "2015 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2015",
}