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Measuring agreement on linguistic expressions in medical treatment scenarios

Navarro, J.; Wagner, C.; Aickelin, U.; Green, L.; Ashford, R.

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Authors

J. Navarro

U. Aickelin

L. Green

R. Ashford



Abstract

Quality of life assessment represents a key process of deciding treatment success and viability. As such, patients’ perceptions of their functional status and well-being are important inputs for impairment assessment. Given that patient completed questionnaires are often used to assess patient status and determine future treatment options, it is important to know the level of agreement of the words used by patients and different groups of medical professionals. In this paper, we propose a measure called the Agreement Ratio which provides a ratio of overall agreement when modelling words through Fuzzy Sets (FSs). The measure has been specifically designed for assessing this agreement in fuzzy sets which are generated from data such as patient responses. The measure relies on using the Jaccard Similarity Measure for comparing the different levels of agreement in the FSs generated. Synthetic examples are provided in order to show how to calculate the measure for given Fuzzy Sets. An application to real-world data is provided as well as a discussion about the results and the potential of the proposed measure.

Citation

Navarro, J., Wagner, C., Aickelin, U., Green, L., & Ashford, R. (2016). Measuring agreement on linguistic expressions in medical treatment scenarios. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI) (1-8). https://doi.org/10.1109/SSCI.2016.7849895

Conference Name 2016 IEEE Symposium Series on Computational Intelligence (SSCI)
Conference Location Athens, Greece
Start Date Dec 6, 2016
End Date Dec 9, 2016
Acceptance Date Sep 29, 2016
Online Publication Date Feb 13, 2017
Publication Date 2016-12
Deposit Date Nov 2, 2016
Publicly Available Date Mar 28, 2024
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Pages 1-8
Book Title 2016 IEEE Symposium Series on Computational Intelligence (SSCI)
ISBN 978-1-5090-4241-8
DOI https://doi.org/10.1109/SSCI.2016.7849895
Keywords Survey data, Computing with words, Interval agreement approach, Similarity, Questionnaires
Public URL https://nottingham-repository.worktribe.com/output/808891
Publisher URL https://ieeexplore.ieee.org/document/7849895/

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