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Using Fuzzy Evidential Reasoning for Multiple Assessment Fusion in Spondylarthropathic Patient Self-management

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Emerging Trends and Advanced Technologies for Computational Intelligence

Part of the book series: Studies in Computational Intelligence ((SCI,volume 647))

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Abstract

This paper proposes an approach for an ICT-supported medical assessment, by merging measures of signs and symptoms from heterogeneous sources. The disease status estimate of patients that suffer from spondylarthropathy is evaluated with different types of uncertainties using a fuzzy rule-based evidential reasoning (FURBER) approach. The approach treats measures of signs and symptoms in order to define the disease status. We take in consideration the Bath indices and the ASDAS index, described by using fuzzy linguistic variables. A fuzzy rule-base designed on the basis of a belief structure is exploited to capture uncertainty and non-linear relationships between these parameters and the disease status. The inference of the rule-based system is implemented using an evidential reasoning algorithm. An expected utility-based health score is used to assess disease activity over time and to measure the response to treatment. Our tool may be particularly helpful in monitoring the response of treatments and in interpreting the response to therapeutic interventions in clinical trials. A case study is used to illustrate the application of the proposed approach.

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Acknowledgments

This work has been supported by the Mobile Musculoskeletal User Self-management (MOSKUS) project funded by Research Council of Norway in the VERDIKT programme, grant number 227251. The authors would like to thank the MOSKUS project consortium for their valuable discussions and medical domain expertise.

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Schiboni, G., Leister, W., Chen, L. (2016). Using Fuzzy Evidential Reasoning for Multiple Assessment Fusion in Spondylarthropathic Patient Self-management. In: Chen, L., Kapoor, S., Bhatia, R. (eds) Emerging Trends and Advanced Technologies for Computational Intelligence. Studies in Computational Intelligence, vol 647. Springer, Cham. https://doi.org/10.1007/978-3-319-33353-3_2

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