Case-Based Reasoning for Medical and Industrial Decision Support Systems

• The amount of medical and industrial experience and knowledge is rapidly growing and it is almost impossible to be up to date with everything. The demand of decision support system (DSS) is especially important in domains where experience and knowledge grow rapidly. However, traditional approaches to DSS are not always easy to adapt to a flow of new experience and knowledge and may also show a limitation in areas with a weak domain theory. This chapter explores the functionalities of Case-Based Reasoning (CBR) to facilitate experience reuse both in clinical and in industrial decision making tasks. Examples from the field of stress medicine and condition monitoring in industrial robots are presented here to demonstrate that the same approach assists both for clinical applications as well as for decision support for engineers. In the both domains, DSS deals with sensor signal data and integrate other artificial intelligence techniques into the CBR system to enhance the performance in a number of different aspects. Textual information retrieval, Rule-based Reasoning (RBR), and fuzzy logic are combined together with CBR to offer decision support to clinicians for a more reliable and efficient management of stress. Agent technology and wavelet transformations are applied with CBR to diagnose audible faults on industrial robots and to package such a system. The performance of the CBR systems have been validated and have shown to be useful in solving such problems in both of these domains.

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