  Clinical decision support systems combine knowledge and data from a variety
of sources, represented by quantitative models based on stochastic methods, or
qualitative based rather on expert heuristics and deductive reasoning. At the
same time, case-based reasoning (CBR) memorizes and returns the experience of
solving similar problems. The cooperation of heterogeneous clinical knowledge
bases (knowledge objects, semantic distances, evaluation functions, logical
rules, databases...) is based on medical ontologies. A multi-agent decision
support system (MADSS) enables the integration and cooperation of agents
specialized in different fields of knowledge (semiology, pharmacology, clinical
cases, etc.). Each specialist agent operates a knowledge base defining the
conduct to be maintained in conformity with the state of the art associated
with an ontological basis that expresses the semantic relationships between the
terms of the domain in question. Our approach is based on the specialization of
agents adapted to the knowledge models used during the clinical steps and
ontologies. This modular approach is suitable for the realization of MADSS in
many areas.
