Abstract
In this paper we present an approach to medical diagnosis based on the collective choice paradigm introduced in the middle of 20th century by Kenneth Arrow. Starting from a GDSS architecture introduced to support collaborative medical decision making we focus on the representation of the consensual dynamics based on the minimization of a cost function and aiming at selecting the most agreeable diagnosis profile. The model is based on a set of individual fuzzy preference relations and combines a soft measure of collective dissensus with an inertial mechanism of opinion changing aversion.
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Bortot, S., Fedrizzi, M. (2013). Medical Decision Making as a Group Choice Process: Consensual Dynamics in Fuzzy Diagnosis. In: Seising, R., Tabacchi, M. (eds) Fuzziness and Medicine: Philosophical Reflections and Application Systems in Health Care. Studies in Fuzziness and Soft Computing, vol 302. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36527-0_21
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DOI: https://doi.org/10.1007/978-3-642-36527-0_21
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