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Architectures Integrating Case-Based Reasoning and Bayesian Networks for Clinical Decision Support

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.
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Tore Bruland, Agnar Aamodt, Helge Langseth. Architectures Integrating Case-Based Reasoning and Bayesian Networks for Clinical Decision Support. 6th IFIP TC 12 International Conference on Intelligent Information Processing (IIP), Oct 2010, Manchester, United Kingdom. pp.82-91, ⟨10.1007/978-3-642-16327-2_13⟩. ⟨hal-01055068⟩

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