Advertisement

Prognosis of Approaching Infectious Diseases

  • Rainer Schmidt
  • Lothar Gierl
Conference paper
  • 442 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2780)

Abstract

Few years ago, we have developed an early warning system concerning multiparametric kidney function courses. As methods we applied Temporal Abstraction and Case-based Reasoning. In our current project we apply very similar ideas. The goal of the TeCoMed project is to compute early warnings against forthcoming waves or even epidemics of infectious diseases in the German federal state of Mecklenburg-Western Pomerania. Furthermore, these warnings shall be sent to interested practitioners, pharmacists etc. We have developed a prognostic model for diseases that are characterised by cyclic, but irregular behaviour. So far, we have applied this model to influenza and bronchitis.

Keywords

Early Warning Early Warning System Prognostic Model Weekly Data Influenza Epidemic 
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.

References

  1. 1.
    Schmidt, R., Pollwein, B., Gierl, L.: Medical Multiparametric Time Course Prognoses Applied to Kidney Function Assessments. Int. J. Med. Inform. 53(2-3), 253–264 (1999)CrossRefGoogle Scholar
  2. 2.
    Schmidt, R., Gierl, L.: Case_based Reasoning for Prognosis of Threatening Influenza Waves. In: Perner, P. (ed.) Advances in Data Mining. LNCS (LNAI), vol. 2394, pp. 99–107. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  3. 3.
    Shahar, Y.: A Framework for Knowledge-Based Temporal Abstraction. Artificial Intelligence 90, 79–133 (1997)zbMATHCrossRefGoogle Scholar
  4. 4.
    Aamodt, A., Plaza, E.: Case-Based Reasoning: foundation issues. Methodological variation- and system approaches. AI Communications 7(1), 39–59 (1994)Google Scholar
  5. 5.
    Nichol, K.L., et al.: The effectiveness of Vaccination against Influenza in Adults. New England Journal of Medicine 333, 889–893 (1995)CrossRefGoogle Scholar
  6. 6.
    Dowdle, W.R.: Informed Consent Nelson-Hall, Inc. Chicago, IIIGoogle Scholar
  7. 7.
    Prou, M., et al.: Exploratory Temporal-Spatial Analysis of Influenza Epidemics in France. In: Flahault, A., et al. (eds.) Abstracts of 3rd International Workshop on Geography and Medicine, Paris, p. 17 (2001)Google Scholar
  8. 8.
    Shindo, N., et al.: Distribution of the Influenza Warning Map by Internet. In: Flahault, A., et al. (eds.) Abstracts of 3rd International Workshop on Geography and Medicine, Paris, p. 16 (2001)Google Scholar
  9. 9.
    Farrington, C.P., Beale, A.D.: The Detection of Outbreaks of Infectious Disease. In: Gierl, L., et al. (eds.) GEOMED 1997, International Workshop on Geomedical Systems, Teubner Stuttgart, pp. 97–117 (1997)Google Scholar
  10. 10.
    Viboud, C., et al.: Forecasting the spatio-temporal spread of influenza epidemics by the method of analogues. In: Abstracts of 22nd Annual Conference of the International Society of Clinical Biostatistics, Stockholm, August 20-24, p. 71 (2001)Google Scholar
  11. 11.
    Lorenz, E.N.: Atmospheric predictability as revealed by naturally occuring analogies. J. Atmos. Sci. p. 26 (1969)Google Scholar
  12. 12.
    Wilke, W., Smyth, B., Cunningham, P.: Using Configuration Techniques for Adaptation. In: Lenz, M., Bartsch-Spörl, B., Burkhard, H.-D., Wess, S. (eds.) Case-Based Reasoning Technology. LNCS (LNAI), vol. 1400, pp. 139–168. Springer, Heidelberg (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Rainer Schmidt
    • 1
  • Lothar Gierl
    • 1
  1. 1.Institut für Medizinische Informatik und BiometrieUniversität RostockRostockGermany

Personalised recommendations