Abstract
Over the last decades, Artificial Intelligence has approached the decision support system design in medical domains by capturing the knowledge and configuring it in knowledge intensive software systems. Model-based diagnosis is one of the techniques which has produced the best results, such as diagnosis intelligent systems in the realm of medicine. In this domain, one of the key factors is the temporal dimension. This variable enormously complicates the design of such systems, and in particular the process of getting a reliable diagnosis solution. This paper presents a Diagnosis Abductive Algorithm based on Fuzzy Temporal Abnormal Model. This algorithm provides a solution for the above problem by the description of its dianosis explanation, allowing an approach based on the Possibility Theory for the evaluation of the diagnosis hypotheses.
This work was supported by the Spanish MEC under project MEDICI (TIC2003-09400-C04-01), the Murcia Regional Government under project PB/46/FS/02, the Spanish MEC under the FPU national plan (grant ref. AP2003-4476), and the SENECA Foundation of the Murcia Reginal Government (grant ref. FPI00911CV02).
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Palma, J., Juárez, J.M., Campos, M., Marín, R. (2005). A Fuzzy Temporal Diagnosis Algorithm and a Hypothesis Discrimination Proposal. In: Mira, J., Álvarez, J.R. (eds) Mechanisms, Symbols, and Models Underlying Cognition. IWINAC 2005. Lecture Notes in Computer Science, vol 3561. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11499220_47
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DOI: https://doi.org/10.1007/11499220_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26298-5
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