PDF herunterladen
Methods Inf Med 1980; 19(04): 205-209
DOI: 10.1055/s-0038-1635282
Original Article
Schattauer GmbH

Diagnoses Generated by Numerical Taxonomic Methods Applied to Standard Blood Variables

DIAGNOSESTELLUNG MIT NUMERISCHEN TAXONOMISCHEN METHODEN UNTER ZUHILFENAHME STANDARDISIERTER BLUTVARIABLEN
L. A. Abbott
1   From the Department of Environmental, Population, and Organismic Biology, University of Colorado, Boulder, Colorado 80309
,
J. B. Mitton
1   From the Department of Environmental, Population, and Organismic Biology, University of Colorado, Boulder, Colorado 80309
› Institutsangaben
Weitere Informationen

Publikationsverlauf

Publikationsdatum:
14. Februar 2018 (online)

    Lizenzen und Reprints

Data taken from the blood of 262 patients diagnosed for malabsorption, elective cholecystectomy, acute cholecystitis, infectious hepatitis, liver cirrhosis, or chronic renal disease were analyzed with three numerical taxonomy (NT) methods : cluster analysis, principal components analysis, and discriminant function analysis. Principal components analysis revealed discrete clusters of patients suffering from chronic renal disease, liver cirrhosis, and infectious hepatitis, which could be displayed by NT clustering as well as by plotting, but other disease groups were poorly defined. Sharper resolution of the same disease groups was attained by discriminant function analysis.

Daten aus dem Blut von 262 Patienten mit den Diagnosen mangelhafte Absorption, Cholezystektomie, akute Cholezystitis, infektiöse Hepatitis, Leberzirrhose oder chronische Nierenerkrankung wurden mit Hilfe von drei numerischen Taxonomie(NT)-Methoden analysiert: Cluster-Analyse, Hauptkomponenten-Analyse und Diskrirninanz-Funktionsanalyse. Die Analyse der Hauptkomponenten ergab diskrete Cluster von Patienten mit chronischen Nierenleiden, Leberzirrhose und infektiöser Hepatitis, welche sowohl durch NT-Clustering als auch durch Plotting dargestellt werden konnten, während andere Patientengruppen schlecht definiert waren. Eine schärfere Abgrenzung der gleichen Patientengruppen wurde durch die Diskrirninanz-Funktionsanalyse erreicht.

Key-Words:

Computer-assisted Diagnosis - Cluster Analysis - Discriminant Function Analysis - Principal Component Analysis - Standard Blood Variables

Schlüssel-Wörter:

Computerunterstützte Diagnose - Clusteranalyse - Diskrirninanz-Funktionsanalyse - Hauptkomponenten-Analyse - standardisierte Blutvariable
 

  • References

  • 1 Alpébovitch A, Leminor M, Lellouch J. The determination of sequential strategies of diagnostic tests : Comparison of different methods by simulation. Meth. Inform. Med 1979; 18: 75-79.
    PubMedGoogle Scholar
  • 2 Alpérovitch A, Fragu P. A suggestion for an effective use of a computer-aided diagnosis system m screening for hyperthyroidism. Meth. Inform. Med 1977; 16: 93-95.
    PubMedGoogle Scholar
  • 3 Baron D. N, Fraser P. M. Medical applications of taxo-nomic methods. Brit. med. Bull 1968; 24: 236-240.
    CrossrefPubMedGoogle Scholar
  • 4 Buttons K. J, Gambino S. R. Laboratory diagnosis by computer. Comput. Biol. Med 1973; 3: 131-136.
    CrossrefPubMedGoogle Scholar
  • 5 Clifford H. T, Stephenson W. An Introduction to Numerical Classification. New York: Academic Press; 1975
    Google Scholar
  • 6 Croft D. J. Is computerized diagnosis possible ? Comput. biomed. Res 1972; 5: 351-367.
    PubMedGoogle Scholar
  • 7 Duran B. S, Lewis T. O. An application of cluster analysis to the construction of a diagnostic classification. Comput. Biol. Med 1974; 4: 183-188.
    CrossrefPubMedGoogle Scholar
  • 8 Ellis G, Goldberg D. M. Discriminant function analysis applied to laboratory tests in patients with hepatobiliary disease. Comput. biomed. Res 1979; 12: 483-501.
    CrossrefPubMedGoogle Scholar
  • 9 Fraser P. M. Precision in Diagnosis : Numerical Taxonomy and Discriminant Analysis. In Atkins H. J. B. (Edit.) : Measurement and Precision in Surgery Oxford: Blackwell; 1969
    Google Scholar
  • 10 Friedman H. P, Goldwyn R. M, Siegel J. H. The use and interpretation of multivariate methods in the classification of stages in serious infectious disease processes in the critically ill. Perspeet. Biol. Med 1975; 1: 81-122.
    PubMedGoogle Scholar
  • 11 Geer Van de J. P. Introduction to Multivariate Analysis for the Social Sciences. San Francisco: W. H. Freeman and Co; 1971
    Google Scholar
  • 12 Goldberg D. M, Ellis G. Mathematical and computer-assisted procedures in the diagnosis of liver and biliary tract disorders. Advonc. clin. Chem 1978; 20: 49-128.
    PubMedGoogle Scholar
  • 13 Gorry G. A, Barnett G. O. Experience with a model of sequential diagnosis. Comput. biomed. Res 1968; 1: 490-507.
    CrossrefPubMedGoogle Scholar
  • 14 Grams R. R, Johnson E. A, Benson E. S. Laboratory data analysis: Section III—Multivariate normality. Amer. J. clin. Path 1972; 58: 188-200.
    PubMedGoogle Scholar
  • 15 Grams R. R, Johnson E. A, Benson E. S. Laboratory data analysis system: Section IV—Multivariate diagnosis. Amer. J. clin. Path 1972; 58: 201-215.
    PubMedGoogle Scholar
  • 16 Hay R. W, Whitehead R. G. Serum-albumin as a prognostic indicator in oedematous malnutrition. Lancet 1975; II: 427-429.
    PubMedGoogle Scholar
  • 17 Hockstra D. J. Sequential games and medical diagnosis. Comput. biomed. Res 1976; 9: 205-215.
    CrossrefPubMedGoogle Scholar
  • 18 Hughes W. L, Kalbfleisch J. M, Brandt Jr. E. N, Costiloe J. P. Myocardial infarction prognosis by discriminant analysis. Arch. int. Med 111 1963; 338-345.
    CrossrefPubMedGoogle Scholar
  • 19 Jardine N, Sibson R. Mathematical Taxonomy. New York—London: Wiley; 1971
    Google Scholar
  • 20 Knapp R. G, Levi S, Lurie D, Westphal M. A computer-generated diagnostic guide: a comparison of statistical diagnosis and clinical diagnosis. Comput. Biol. Med 1977; 7: 223-230.
    CrossrefPubMedGoogle Scholar
  • 21 Ledley R. S, Lusted L. B. Reasoning foundations of medical diagnosis. Science 1959; 130: 9-21.
    CrossrefPubMedGoogle Scholar
  • 22 Mcneil B. J, Sherman H. Example: Bayesian calculations for the determination of the etiology of pleuritic chest pain in young adults in a teaching hospital, Part B. Comput. biomed. Res 1978; 11: 187-194.
    CrossrefPubMedGoogle Scholar
  • 23 Morrison D. R. Multivariate Statistical Methods. New York: Mcgraw-Hill; 1967
    Google Scholar
  • 24 Reece R. L, Hobbie R. K. Computer evaluation of chemistry values: a reporting and diagnostic aid. Amer. J. clin. Path 1971; 57: 664-675.
    PubMedGoogle Scholar
  • 25 Shea G. An analysis of the Bayes’ procedure for diagnosing multistage diseases. Comput. biomed. Res 1978; 11: 65-75.
    CrossrefPubMedGoogle Scholar
  • 26 Sneath P.H.A. The application of numerical taxonomy to medical problems. In Mathematics and Computer Science in Biology and Medicine London: H. M. S. O; 1965: 81-91.
    Google Scholar
  • 27 Sneath P. H. A, Sokal R. R. Numerical Taxonomy. San Francisco: Freeman and Co; 1973
    Google Scholar
  • 28 Sokal R. R, Sneath P.H.A. Principles of Numerical Taxonomy. San Francisco: Freeman and Co; 1962
    Google Scholar
  • 29 Sokal R. R. Numerical Taxonomy and Disease Classification. In Jacquez J. A. (Edit.): The Diagnostic Process Ann Arbor: Malloy Lithographing; 1964: 51-68.
    Google Scholar
  • 30 Spicer C. C, Jones J. H, Jones J. E.L. Discriminant and Bayes analysis in the differential diagnosis of Crohns disease and proctocolitis. Meth. Inform. Med 1973; 12: 118-122.
    PubMedGoogle Scholar
  • 31 Warner H. R, Tobonto A. F, Veasey L. G. A mathematical approach to medical diagnosis. J. Amer. med. Ass 1961; 177: 177-183.
    CrossrefPubMedGoogle Scholar
  • 32 Wartak J. Mathematical model for medical diagnosis. Comput. Biol. Med 1974; 4: 79-84.
    CrossrefPubMedGoogle Scholar
  • 33 Winkel P. Patterns and clusters — Multivariate approach for interpreting clinical chemistry results. Clin. Chem 1973; 19: 1329-1338.
    PubMedGoogle Scholar
  • 34 Winkel P, Paldam M, Tygstrup N. Numerical taxonomic analysis of symptoms and signs in 400 patients with cirrhosis of the liver. Comput. biomed. Res 1971; 3: 657-665.
    PubMedGoogle Scholar
  • 35 Winkel P, Tygsteup N. Numerical taxonomic analysis of cirrhosis. II. Clinical significance of classifications. Comput. biomed. Res 1974; 7: 117-126.
    PubMedGoogle Scholar
  • 36 Winkel P. (and Copenhagen Study Group for Liver Diseases): Numerical taxonomic analysis of cirrhosis. I. The Effect of the number and type of variables used. Comput. biomed. Res 1974; 7: 100-110.
    CrossrefPubMedGoogle Scholar
  • 37 Zajicek G, Maayan Ch., Rosenmann E. The application of cluster analysis to glomerular histopathy. Comput. biomed. Res 1977; 10: 471-481.
    CrossrefPubMedGoogle Scholar
  • 38 Zoltie N, Horrocks J. C, de Dombal F. T. Computer-assisted diagnosis of dyspepsia. Meth. Inform. Med 1977; 16: 89-92.
    PubMedGoogle Scholar