Architectures Integrating Case-Based Reasoning and Bayesian Networks for Clinical Decision Support
- Cite this paper as:
- 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
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.