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Summary: Architectures Integrating Case-Based Reasoning
and Bayesian Networks for Clinical Decision
Support
Tore Bruland, Agnar Aamodt, and Helge Langseth
The Norwegian University of Science and Technology (NTNU)
NO-7491 Trondheim, Norway
Abstract In this paper we discuss different architectures for reasoning
under uncertainty related to our ongoing research into building a medical
decision support system. The uncertainty in the medical domain can be
divided into a well understood part and a less understood part. This
motivates the use of a hybrid decision support system, and in particular,
we argue that a Bayesian network should be used for those parts of the
domain that are well understood and can be explicitly modeled, whereas
a case-based reasoning system should be employed to reason in parts of
the domain where no such model is available. Four architectures that
combine Bayesian networks and case-based reasoning are proposed, and
our working hypothesis is that these hybrid systems each will perform
better than either framework will do on its own.
1 Introduction
The field of knowledge-based systems has over the years become a mature field.
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