1887

Abstract

SUMMARY: The methods incorporated in the computer program used in a trial of computer-aided identification of bacteria are described. The identification method is based on Bayes's theorem and allows for dependent tests and missing data in the probability matrix. It was found useful in developing the method to take account of the occurrence of errors in bacteriological testing. The method suggests a definite identification only if the Bayesian probability of one of the taxa exceeds a threshold level; if not, a separate procedure selects the best tests to continue the identification.

Loading

Article metrics loading...

/content/journal/micro/10.1099/00221287-77-2-317
1973-08-01
2024-11-06
Loading full text...

Full text loading...

/deliver/fulltext/micro/77/2/mic-77-2-317.html?itemId=/content/journal/micro/10.1099/00221287-77-2-317&mimeType=html&fmt=ahah

References

  1. Anderson J. A. 1968; Constrained discrimination between k populations. Journal of the Royal Statistical Society B 31:123–139
    [Google Scholar]
  2. Anderson J. A., Boyle J. A. 1968; Computer diagnosis: statistical aspects. British Medical Bulletin 24:230–235
    [Google Scholar]
  3. Bascomb S., Lapage S. P., Curtis M. A., Willcox W. R. 1973; Identification of bacteria by computer: identification of reference strains. Journal of General Microbiology 77:291–315
    [Google Scholar]
  4. Boyle J. A., Greig W. R., Franklin D. A., Harden R. M., Buchanan W. W., McGirr E. M. 1966; Construction of a model for computer assisted diagnosis: application to the problem of non-toxic goitre. Quarterly Journal of Medicine 35:565–588
    [Google Scholar]
  5. Card W. I., Good I. J. 1970; The estimation of the implicit utilities of medical consultants. Mathematical Biosciences 6:37–44
    [Google Scholar]
  6. Dickey J. M. 1968; Estimation of disease probabilities conditional on symptom variables. Mathematical Biosciences 3:249–265
    [Google Scholar]
  7. Dybowski W., Franklin D. A. 1968; Conditional probability and the identification of bacteria: a pilot study. Journal of General Microbiology 54:215–229
    [Google Scholar]
  8. Edwards A. W. F. 1969; Statistical methods in scientific inference. Nature, London 222:1233–1237
    [Google Scholar]
  9. Good I. J. 1959; Kinds of probability. Science, New York 129:443–447
    [Google Scholar]
  10. Good I. J. 1965 The Estimation of Probabilities Cambridge: M.I.T. Press;
    [Google Scholar]
  11. Good I. J. 1970; Some statistical methods in machine intelligence research. Mathematical Biosciences 6:185–208
    [Google Scholar]
  12. Gorry G. A. 1968; Strategies for computer-aided diagnosis. Mathematical Biosciences 2:293–318
    [Google Scholar]
  13. Gower J. C., Barnett J. A. 1971; Selecting tests in diagnostic keys with unknown responses. Nature, London 232:491–493
    [Google Scholar]
  14. Gyllenberg H. 1963; A general method for deriving determination schemes for random collections of microbial isolates. Annales Academiae scientiarum fennicae A iv 69:1–23
    [Google Scholar]
  15. Hill L. R., Silvestri L. G. 1962; Quantitative methods in the systematics of Actinomycetales. III. The taxonomic significance of physiological-biochemical characters and the construction of a diagnostic key. Giornale di Microbiologia 10:1–28
    [Google Scholar]
  16. Hills M. 1967; Discrimination and allocation with discrete data. Applied Statistics 16:237–250
    [Google Scholar]
  17. Kendall M. G., Buckland W. R. 1957 A Dictionary of Statistical Terms Edinburgh and London: Oliver and Boyd;
    [Google Scholar]
  18. Kendall M. G., Stuart A. 1963 The Advanced Theory of Statistics vol 1 pp 198–201 London: Griffin;
    [Google Scholar]
  19. Kendall M. G., Stuart A. 1968 The Advanced Theory of Statistics vol 3 p 314 London: Griffin;
    [Google Scholar]
  20. Lapage S. P., Bascomb S., Willcox W. R., Curtis M. A. 1973; Identification of bacteria by computer: general aspects and perspectives. Journal of General Microbiology 77:273–290
    [Google Scholar]
  21. Ledley R. S., Lusted L. B. 1959; Reasoning foundations of medical diagnosis. Science, New York 130:9–21
    [Google Scholar]
  22. Möller F. 1962; Quantitative methods in the systematics of Actinomycetales. IV. The theory and application of a probabilistic identification key. Giornale di Microbiologia 10:29–47
    [Google Scholar]
  23. Morse L. E. 1971; Specimen identification and key construction with timesharing computers. Taxon 20:269–282
    [Google Scholar]
  24. Niemelä S. I., Hopkins J. W., Quadling C. 1968; Selecting an economical binary test battery for a set of microbial cultures. Canadian Journal of Microbiology 14:271–279
    [Google Scholar]
  25. Nugent C. A., Warner H. R., Dunn J. T., Tyler F. H. 1964; Probability theory in the diagnosis of Cushing’s syndrome. Journal of Clinical Endocrinology and Metabolism 24:621–627
    [Google Scholar]
  26. Pankhurst R. J. 1970; Acomputer program for generating diagnostic keys. Computer Journal 13:145–151
    [Google Scholar]
  27. Rypka E. W., Clapper W. E., Bowen I. G., Babb R. 1967; A model for the identification of bacteria. Journal of General Microbiology 46:407–424
    [Google Scholar]
  28. Sneath P. H. A., Johnson R. 1972; The influence on numerical taxonomic similarities of errors in microbiological tests. Journal of General Microbiology 72:377–392
    [Google Scholar]
  29. Willcox W. R., Lapage S. P. 1972; Automatic construction of diagnostic tables. Computer Journal 15:263–267
    [Google Scholar]
/content/journal/micro/10.1099/00221287-77-2-317
Loading
/content/journal/micro/10.1099/00221287-77-2-317
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error