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Matheny ME, Fitzhenry F, Speroff T, Hathaway J, Murff HJ, Brown SH, Fielstein EM, Dittus RS, Elkin PL. Detection of blood culture bacterial contamination using natural language processing. AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium. 2009 Nov 14; 2009:411-5.
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%.