A blackboard architecture for control
Artificial Intelligence
The role of frame-based representation in reasoning
Communications of the ACM
Evidential reasoning using stochastic simulation of causal models
Artificial Intelligence
Decision theory in expert systems and artificial intelligence
International Journal of Approximate Reasoning
Intelligent decision systems
NEOANEMIA: a knowledge-based system emulating diagnostic reasoning
Computers and Biomedical Research
A representation for gaining insight into clinical decision models
A representation for gaining insight into clinical decision models
Knowledge and intelligent computing system in medicine
Computers in Biology and Medicine
Expert Systems with Applications: An International Journal
European research efforts in medical knowledge-based systems
Artificial Intelligence in Medicine
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The design and development of a knowledge-based system (KBS) for therapy planning may benefit from an epistemological analysis of this generic medical task. We specialized a previously formulated epistemological model of medical reasoning toward therapy planning by defining an appropriate ontology and an inference model. We propose a computational framework for the implementation of such epistemological model. Then, we discuss how to choose the formalisms for knowledge representation. It will become evident that a KBS for therapy planning usually requires more than one formalism. We experimented the combined use of production rules, frames, and probabilistic models, such as influence diagrams. From the computational point of view, the system is based on a blackboard control architecture, where control knowledge and domain knowledge are represented explicitly and separately. The medical domain where the system has been experimented is hematology, more specifically the therapy of anemic patients. Clinical examples from this field provide empirical evidence supporting our claims.