Conversational CBR for Improved Patient Information Acquisition
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In this thesis we describe our study of two knowledge intensive Conversational Case-Based Reasoning (CCBR) systems and their methods. We look in particular at the way they have solved inferencing and question ranking. Then we continue with a description of our own design for a CCBR system, that will help patients share their experiences of side effects with drugs, with other patients. We describe how we create cases, how our question selection methods work and present an example of how the domain model will look. It is also included a simulation of how a dialogue would be for a patient. The design we have created is a good basis for implementing a knowledge intensive CCBR system. The system should work better than a normal CCBR system, because of the inferencing and question ranking methods, which should lessen the cognitive load on the user and require fewer questions answered, to reach a good solution.