Integrating Case-based and Bayesian Reasoning for Decision Support
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In this thesis, we present an approach to integration of case-based reasoning and Bayesian reasoning for decision support. Our design is meant to provide physicians with decision support in the context of palliative care for lung cancer patients. Because of delays in the medical data, we created an intermediate application with the aim to assist people in choosing an adequate wine for a given meal. We have developed a system that is able to utilize both the general knowledge of the Bayesian network and the specialized knowledge of the case base. Our results shows that the combination of CBR and BN are able to discover solutions that would not been found by using only one of the methodologies.