Effect of atypical antibiotic resistance on microorganism identification by pattern recognition.

Abstract:

:We classified microorganisms from the clinical laboratory by using information provided by the Gram stain and antibiotic sensitivity profiles obtained with the Bauer-Kirby technique. Approximately 4,000 microorganisms, routinely identified and tested for antibiotic sensitivities in a large hospital microbiology laboratory, were used as a data set for several pattern recognition classification methods: K--nearest-neighbor analysis, statistical isolinear multicomponent analysis, Bayesian inference, and linear discriminant analysis. K--nearest-neighbor analysis yielded the highest prospective classification accuracy for gram-negative organisms, 90%. When those organisms displaying an atypical antibiotic resistance pattern were excluded from the data, the gram-negative classification accuracy improved to 95%. These results are inferior to currently accepted biochemical identification methods. Microorganisms with atypical antibiotic resistance patterns are likely to be misidentified and are common enough (17% of our isolates) to limit the feasibility of routine identification of microorganisms from their antibiotic sensitivities.

journal_name

J Clin Microbiol

authors

Boyd JC,Lewis JW,Marr JJ,Harper AM,Kowalski BR

subject

Has Abstract

pub_date

1978-12-01 00:00:00

pages

689-94

issue

6

eissn

0095-1137

issn

1098-660X

journal_volume

8

pub_type

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