title: 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.
mesh_terms: D000900:Anti-Bacterial Agents / Q000494:pharmacology*; D001419:Bacteria / Q000145:classification* / Q000187:drug effects; D001424:Bacterial Infections / Q000382:microbiology*; D004352:Drug Resistance, Microbial; D006801:Humans; D010363:Pattern Recognition, Automated
chemical_list: D000900:Anti-Bacterial Agents