Measuring agreement on linguistic expressions in medical treatment scenarios

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

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, December). Measuring agreement on linguistic expressions in medical treatment scenarios. Presented at 2016 IEEE Symposium Series on Computational Intelligence (SSCI), Athens, Greece

• Presentation Conference Type: Edited Proceedings
• Conference Name: 2016 IEEE Symposium Series on Computational Intelligence (SSCI)
• 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: Dec 31, 2016
• 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/
• Contract Date: Nov 2, 2016