Hybrid Inference Components for Monitoring of Artificial Respiration

  • K. Gärtner
  • S. Fuchs
  • H. Jauch
Part of the Research Reports ESPRIT book series (ESPRIT, volume 1)


Medical expert systems are developed for supporting the complicated decision finding processes of the physicians. Generally, the decision finding in a clinical therapeutic process contains a complex data fusion problem. The problem is analysed and the conclusions are used for the development of medical expert systems.

The indentified pattern of problem solving behavior of the experts allows the structuring of the knowledge in sections. Different data structures need different representations and inference strategies. The higher organized expense for the data fusion is solved by implementation of a blackboard structure.

Finally, hybrid inference components for monitoring of artificial ventilation are proposed.


Fuzzy Inference System Data Fusion Therapeutic Process Artificial Ventilation Ventilator Parameter 
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.


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  1. 1.
    Langlotz C.P., Fagan L.M., Tu S.W., Sikic B.I., Shortliffe E.H., “A Therapy Planning Architecture that combines Decision Theory and Artificial Intelligence Techniques”, In: Computers and Biomedical Research 20, S. 279–303, 1987.Google Scholar
  2. 2.
    Puppe F., “Problemlösungsmethoden mit Expertensystemen”, Springer Berlin, 1987.Google Scholar
  3. 3.
    Fagan L.M., “Representing Time-Depending Relations in a Medical Setting”, USA, Standford University, Doctoral thesis (Ph.D.), 1980.Google Scholar
  4. 4.
    Gärtner K., “Problemlösung für die Regelung der maschinellen Beatmung auf Intensivstationen unter Nutzung der rechnergestützten Entschei-dungsfindung”, Diss., IH Dresden, 1984.Google Scholar
  5. 5.
    Schreiber Th., “Beratungssystem für die Beatmungsüberwachung unter Nutzung statistischer und wissensbasierter Problemlösungsmethoden”, Diss., Fakultät Informatik, TU Dresden, 1990.Google Scholar
  6. 6.
    Jauch H., “Konzipierung einer Inferenzkomponente für das medizinische Beratungssystem IBEUS”, Diplomarbeit, Informatik, TU Dresden, 1992.Google Scholar
  7. 7.
    Kaiser S., Morgenstern U., “Einsatzmöglichkeiten von Modellen der Ventilationsmechanik bei der maschinellen Beatmung”, Wissenschaftliche Beiträge der IH Dresden, Heft 5,1986.Google Scholar
  8. 8.
    Grohmann U., “Konzipierung und Realisierung einer Lernkomponente für das medizinische Beratungssystem IBEUS”, Belegarbeit, Informatik, TU Dresden, 1992.Google Scholar
  9. 9.
    Pöthig A., “Wissensbasis-prototyping in der klinischen Praxis anhand der ARDS-Grundwissensbasis”, Diplomarbeit, Informatik, TU Dresden, 1990.Google Scholar
  10. 10.
    Brandt A., “Auswertung metrischer Stichprobendaten unter Anwendung clusteranalytischer Verfahren”, Diplomarbeit, Informatik, TU Dresden, 1992.Google Scholar

Copyright information

© ECSC-EEC-EAEC, Brussels-Luxembourg 1993

Authors and Affiliations

  • K. Gärtner
    • 1
  • S. Fuchs
    • 1
  • H. Jauch
    • 1
  1. 1.Institute of Artificial IntelligenceDresden University of TechnologyGermany

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