Literature DB >> 4751789

Computer identification of bacteria on the basis of their antibiotic susceptibility patterns.

R Friedman, J MacLowry.   

Abstract

A computer program utilizing a Baysean mathematical model was developed to identify bacteria solely on the basis of their antibiotic sensitivities. The model contains probability data on the antibiotic sensitivity patterns for 31 species of bacteria, which account for over 99% of all isolates submitted to our laboratory for testing. During a 4-month test period, antibiotic sensitivity data on 1,000 clinical isolates were processed by the program. The identification achieved by using the model was the same as that of the laboratory for over 86% of the isolates.

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Year:  1973        PMID: 4751789      PMCID: PMC379781          DOI: 10.1128/am.26.3.314-317.1973

Source DB:  PubMed          Journal:  Appl Microbiol        ISSN: 0003-6919


  4 in total

1.  Detailed methodology and implementation of a semiautomated serial dilution microtechnique for antimicrobial susceptibility testing.

Authors:  J D MacLowry; M J Jaqua; S T Selepak
Journal:  Appl Microbiol       Date:  1970-07

2.  A diagnostic schema for identifying enterobacteriaceae and miscellaneous gram-negative bacilli.

Authors:  G J Domingue; F Dean; J R Miller
Journal:  Am J Clin Pathol       Date:  1969-01       Impact factor: 2.493

3.  Conditional probability and the identification of bacteria: a pilot study.

Authors:  W Dybowski; D A Franklin
Journal:  J Gen Microbiol       Date:  1968-12

4.  On-line computer quality control of antibiotic-sensitivity testing.

Authors:  J Petralli; E Russell; A Kataoka; T C Merigan
Journal:  N Engl J Med       Date:  1970-10-01       Impact factor: 91.245

  4 in total
  9 in total

1.  Computer-assisted bacterial identification utilizing antimicrobial susceptibility profiles generated by autobac 1.

Authors:  B H Sielaff; E A Johnson; J M Matsen
Journal:  J Clin Microbiol       Date:  1976-02       Impact factor: 5.948

2.  Discriminant analysis of antibiotic susceptibility as a means of bacterial identification.

Authors:  G Darland
Journal:  J Clin Microbiol       Date:  1975-11       Impact factor: 5.948

3.  Mathematical analysis of the API enteric 20 profile register using a computer diagnostic model.

Authors:  E A Robertson; J D MacLowry
Journal:  Appl Microbiol       Date:  1974-10

4.  Diagnostic probability matrix for identification of slowly growing mycobacteria in clinical laboratories.

Authors:  L G Wayne; M I Krichevsky; D Portyrata; C K Jackson
Journal:  J Clin Microbiol       Date:  1984-10       Impact factor: 5.948

5.  Automated, rapid identification of bacteria by pattern analysis of growth inhibition profiles obtained with Autobac 1.

Authors:  G E Buck; B H Sielaff; R Boshard; J M Matsen
Journal:  J Clin Microbiol       Date:  1977-07       Impact factor: 5.948

6.  Accuracy and precision of the autobac system for rapid identification of Gram-negative bacilli: a collaborative evaluation.

Authors:  A L Barry; T L Gavan; P B Smith; J M Matsen; J A Morello; B H Sielaff
Journal:  J Clin Microbiol       Date:  1982-06       Impact factor: 5.948

7.  Novel approach to bacterial identification that uses the autobac system.

Authors:  B H Sielaff; J M Matsen; J E McKie
Journal:  J Clin Microbiol       Date:  1982-06       Impact factor: 5.948

Review 8.  A review of numerical methods in bacterial identification.

Authors:  W R Willcox; S P Lapage; B Holmes
Journal:  Antonie Van Leeuwenhoek       Date:  1980       Impact factor: 2.271

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

Authors:  J C Boyd; J W Lewis; J J Marr; A M Harper; B R Kowalski
Journal:  J Clin Microbiol       Date:  1978-12       Impact factor: 5.948

  9 in total

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