Abstract
Laboratory error detection is a hard task yet plays an important role in efficient care of the patients. Quality controls are inadequate in detecting pre-analytic errors and are not frequent enough. Hence population- and patient-based detectors are developed. However, it is not clear what set of analytes leads to the most efficient error detectors. Here, we use three different scoring functions that can be used in detecting errors, to rank a set of analytes in terms of their strength in distinguishing erroneous measurements. We also observe that using evaluations of larger subsets of analytes in our analysis does not necessarily lead to a more accurate error detector. In our data set obtained from renal kidney disease inpatients, calcium, potassium, and sodium, emerged as the top-3 indicators of an erroneous measurement. Using the joint likelihood of these three analytes, we obtain an estimated AUC of 0.73 in error detection.
| Original language | English (US) |
|---|---|
| Title of host publication | 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 5793-5796 |
| Number of pages | 4 |
| Volume | 2016-October |
| ISBN (Electronic) | 9781457702204 |
| DOIs | |
| State | Published - Oct 13 2016 |
| Event | 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 - Orlando, United States Duration: Aug 16 2016 → Aug 20 2016 |
Other
| Other | 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 |
|---|---|
| Country | United States |
| City | Orlando |
| Period | 8/16/16 → 8/20/16 |
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ASJC Scopus subject areas
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics
Cite this
Assessing subsets of analytes in context of detecting laboratory errors. / Sourati, J.; Kazmierczak, Steven (Steve); Akcakaya, M.; Dy, J. G.; Leen, T. K.; Erdogmus, D.
2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. Vol. 2016-October Institute of Electrical and Electronics Engineers Inc., 2016. p. 5793-5796 7592044.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Assessing subsets of analytes in context of detecting laboratory errors
AU - Sourati, J.
AU - Kazmierczak, Steven (Steve)
AU - Akcakaya, M.
AU - Dy, J. G.
AU - Leen, T. K.
AU - Erdogmus, D.
PY - 2016/10/13
Y1 - 2016/10/13
N2 - Laboratory error detection is a hard task yet plays an important role in efficient care of the patients. Quality controls are inadequate in detecting pre-analytic errors and are not frequent enough. Hence population- and patient-based detectors are developed. However, it is not clear what set of analytes leads to the most efficient error detectors. Here, we use three different scoring functions that can be used in detecting errors, to rank a set of analytes in terms of their strength in distinguishing erroneous measurements. We also observe that using evaluations of larger subsets of analytes in our analysis does not necessarily lead to a more accurate error detector. In our data set obtained from renal kidney disease inpatients, calcium, potassium, and sodium, emerged as the top-3 indicators of an erroneous measurement. Using the joint likelihood of these three analytes, we obtain an estimated AUC of 0.73 in error detection.
AB - Laboratory error detection is a hard task yet plays an important role in efficient care of the patients. Quality controls are inadequate in detecting pre-analytic errors and are not frequent enough. Hence population- and patient-based detectors are developed. However, it is not clear what set of analytes leads to the most efficient error detectors. Here, we use three different scoring functions that can be used in detecting errors, to rank a set of analytes in terms of their strength in distinguishing erroneous measurements. We also observe that using evaluations of larger subsets of analytes in our analysis does not necessarily lead to a more accurate error detector. In our data set obtained from renal kidney disease inpatients, calcium, potassium, and sodium, emerged as the top-3 indicators of an erroneous measurement. Using the joint likelihood of these three analytes, we obtain an estimated AUC of 0.73 in error detection.
UR - http://www.scopus.com/inward/record.url?scp=85009152783&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85009152783&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2016.7592044
DO - 10.1109/EMBC.2016.7592044
M3 - Conference contribution
VL - 2016-October
SP - 5793
EP - 5796
BT - 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
ER -