Knowledge representation and diagnostic inference using Bayesian
  networks in the medical discourse
  For the diagnostic inference under uncertainty Bayesian networks are
investigated. The method is based on an adequate uniform representation of the
necessary knowledge. This includes both generic and experience-based specific
knowledge, which is stored in a knowledge base. For knowledge processing, a
combination of the problem-solving methods of concept-based and case-based
reasoning is used. Concept-based reasoning is used for the diagnosis, therapy
and medication recommendation and evaluation of generic knowledge. Exceptions
in the form of specific patient cases are processed by case-based reasoning. In
addition, the use of Bayesian networks allows to deal with uncertainty,
fuzziness and incompleteness. Thus, the valid general concepts can be issued
according to their probability. To this end, various inference mechanisms are
introduced and subsequently evaluated within the context of a developed
prototype. Tests are employed to assess the classification of diagnoses by the
network.
