Measuring agreement on linguistic expressions in medical treatment scenarios
This addresses the need for reliable communication in medical treatment decisions, but it appears incremental as it adapts existing similarity methods to a specific domain.
The paper tackles the problem of measuring agreement between patients and medical professionals on linguistic expressions in quality-of-life assessments by proposing an Agreement Ratio measure based on Fuzzy Sets and Jaccard Similarity, though no concrete numerical results are provided.
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.