Measuring agreement on linguistic expressions in medical treatment scenarios

TL;DR

This paper introduces the Agreement Ratio, a new measure based on Fuzzy Sets and Jaccard Similarity, to quantify agreement levels among patients and medical professionals on linguistic expressions in treatment scenarios.

Contribution

It proposes a novel agreement measure tailored for fuzzy set representations of patient responses, enhancing assessment of perceptual consensus in medical contexts.

Findings

The Agreement Ratio effectively quantifies agreement levels.

The measure is specifically designed for fuzzy set data.

It utilizes Jaccard Similarity for comparison.

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.