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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Keywords
- Bayesian Network
- Retrieve Phase
- Bayesian Network Model
- Conditional Probability Table
- Aleatory Uncertainty
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Bruland, T., Aamodt, A., Langseth, H. (2010). Architectures Integrating Case-Based Reasoning and Bayesian Networks for Clinical Decision Support. In: Shi, Z., Vadera, S., Aamodt, A., Leake, D. (eds) Intelligent Information Processing V. IIP 2010. IFIP Advances in Information and Communication Technology, vol 340. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16327-2_13
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DOI: https://doi.org/10.1007/978-3-642-16327-2_13
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