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Case-Based Reasoning for Medical and Industrial Decision Support Systems

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Successful Case-based Reasoning Applications - I

Part of the book series: Studies in Computational Intelligence ((SCI,volume 305))

Abstract

The amount of medical and industrial experience and knowledge is rapidly growing and it is increasingly difficult to be up to date leading to less informed decisions, mistakes and serious accidents. The demand for 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 clinical applications as well as decision support for engineers. In both domains, DSS deals with sensor signal data and integrate other artificial intelligence techniques into the CBR system to enhance 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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Authors and Affiliations

  1. School of Innovation, Design and Engineering, Mälardalen University, P.O. Box 883, SE-721 23, Västerås, Sweden

    Mobyen Uddin Ahmed, Shahina Begum, Erik Olsson, Ning Xiong & Peter Funk

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  1. Mobyen Uddin Ahmed
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  2. Shahina Begum
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  3. Erik Olsson
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  4. Ning Xiong
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  5. Peter Funk
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Editors and Affiliations

  1. Universita’ del Piemonte Orientale, Alessandria, Italy

    Stefania Montani

  2. University of South Australia, Adelaide, Australia

    Lakhmi C. Jain

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Ahmed, M.U., Begum, S., Olsson, E., Xiong, N., Funk, P. (2010). Case-Based Reasoning for Medical and Industrial Decision Support Systems. In: Montani, S., Jain, L.C. (eds) Successful Case-based Reasoning Applications - I. Studies in Computational Intelligence, vol 305. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14078-5_2

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  • DOI: https://doi.org/10.1007/978-3-642-14078-5_2

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