Advertisement

A Computer Aided System of Improving Therapeutic Measures by Long Term Follow-Up

  • Lothar Horbach
Conference paper
Part of the Lecture Notes in Medical Informatics book series (LNMED, volume 11)

Summary

A computer aided system of follow-up observations of defined diseases is implemented. Information about the individual pattern of findings at the onset of therapy and during the course of disease leading to good or poor results is documented. The system supports the communication of information to the different medical services involved. On the base of the documented data the parameters of multivariate models of prognosis are estimated. By the aid of these models the attribution of future patients to different prognostic groups is quantified and gives useful advice in therapeutic decisions.

Keywords

Therapeutic Decision Therapeutic Measure Medical Informatics mUltivariate Normal Distribution Observation Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Bibliography

  1. (1).
    Horbach L, überla K. Rahmenplanung zum Einsatz der elektronischen Datenverarbeitung für die Medizinischen Fachbereiche der Bayerischen Universitäten. Erlangen 1977Google Scholar
  2. (2).
    Meyer-Bender BA, Greiller R, Horbach L, Lange H-J, Seidel H, überla K. Interfaces in a Computer Network for the Medical Schools in Bavaria. Lecture Notes in Medical Informatics Nr. 5, Proceedings of Medical Informatics, Berlin, Springer, p.763–773Google Scholar
  3. (3).
    Horbach L, Gunselmann W, Just H. Schicketanz KU, Schmidt W. Verl aufsindizes bei Herzinfarkten.Bericht 19. Jahrestagung der Dtsch. Ges. f. Med. Dok. u. Statistik, Mainz 1974, Schättauer, StuttgartGoogle Scholar
  4. (4).
    Horbach L, Just H. Klinisch-therapeutisehe Studie: Trasylol bei Herzinfarkt. Intensivmedizin 1979; 16: 338–360Google Scholar
  5. (5).
    Hughes WL, Kalbfleisch JM, Brandt EN, Costiloc JP. Myocardial infarction prognosis by discriminant analysis. Arch.Intern.Med. 1963; III: 338CrossRefGoogle Scholar
  6. (6).
    Norris RM, Brandt PWT, Caughey DE, Lee AJ, Scott PJ. A new coronary prognostic index. Lancet 1967: 274–278.Google Scholar
  7. (7).
    Cornfield J. Joint dependence of risk of coronary heart disease. on serum cholesterol and systolic blood pressure: a discriminant function analysis. Fed.Proc. 1962; 21: 58–61Google Scholar
  8. (8).
    Walker SH, Duncan DB. Estimation of the probability of an event as a function of several independant variables. Biometrika 1967; 54: 167–179MathSciNetMATHGoogle Scholar
  9. (9).
    Cox DR. Regression models and life tables (with discussion). J.R. Statist-iSoc. 1972; B 34: 187–220Google Scholar
  10. (10).
    Kaibfleisch JD, Prentie RL. Marginal likelihoods based on Cox’s regression and life model. Biometrika 1973; 60: 267–278MathSciNetCrossRefGoogle Scholar
  11. (11).
    Gunselmann W. Multivariate Prognosemodelle in der Medizin. Habilitationsschrift 1979, ErlangenGoogle Scholar
  12. (12).
    Hermanek P. “Grading” und “Staging”. Bedeutung für die klinische Onkologie. Fortschr der Medizin 1978; 96: 520–524.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1981

Authors and Affiliations

  • Lothar Horbach
    • 1
  1. 1.Institute for Medical Statistics and DocumentationErlangenGermany

Personalised recommendations