Detection of blood culture bacterial contamination using natural language processing.
Microbiology results are reported in semi-structured formats and have a high content of useful patient information. We developed and validated a hybrid regular expression and natural language processing solution for processing blood culture microbiology reports. Multi-center Veterans Affairs training and testing data sets were randomly extracted and manually reviewed to determine the culture and sensitivity as well as contamination results. The tool was iteratively developed for both outcomes using a training dataset, and then evaluated on the test dataset to determine antibiotic susceptibility data extraction and contamination detection performance. Our algorithm had a sensitivity of 84.8% and a positive predictive value of 96.0% for mapping the antibiotics and bacteria with appropriate sensitivity findings in the test data. The bacterial contamination detection algorithm had a sensitivity of 83.3% and a positive predictive value of 81.8%.
Matheny
Michael E
ME
GRECC and Center for Health Services Research, Veterans Affairs Tennessee Valley Health System, Nashville, TN; Division of General Internal Medicine and Public Health, Vanderbilt University MedicalCenter, Nashville, TN; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
Fitzhenry
Fern
F
Speroff
Theodore
T
Hathaway
Jacob
J
Murff
Harvey J
HJ
Brown
Steven H
SH
Fielstein
Elliot M
EM
Dittus
Robert S
RS
Elkin
Peter L
PL
D000465:Algorithms; D001431:Bacteriological Techniques; D001769:Blood / Q000382:microbiology*; D005188:False Negative Reactions; D005189:False Positive Reactions; D006801:Humans; D008826:Microbial Sensitivity Tests; D009323:Natural Language Processing