Abstract
BackgroundDespite the vagueness and uncertainty that is intrinsic in any medical act, interpretation and decision (including acts of data reporting and representation of relevant medical conditions), still little research has focused on how to explicitly take this uncertainty into account. In this paper, we focus on the representation of a general and wide-spread medical terminology, which is grounded on a traditional and well-established convention, to represent severity of health conditions (for instance, pain, visible signs), ranging from Absent to Extreme. Specifically, we will study how both potential patients and doctors perceive the different levels of the terminology in both quantitative and qualitative terms, and if the embedded user knowledge could improve the representation of ordinal values in the construction of machine learning models.MethodsTo this aim, we conducted a questionnaire-based research study involving a relatively large sample of 1,152 potential patients and 31 clinicians to represent numerically the perceived meaning of standard and widely-applied labels to describe health conditions. Using these collected values, we then present and discuss different possible fuzzy-set based representations that address the vagueness of medical interpretation by taking into account the perceptions of domain experts. We also apply the findings of this user study to evaluate the impact of different encodings on the predictive performance of common machine learning models in regard to a real-world medical prognostic task.ResultsWe found significant differences in the perception of pain levels between the two user groups. We also show that the proposed encodings can improve the performances of specific classes of models, and discuss when this is the case.ConclusionsIn perspective, our hope is that the proposed techniques for ordinal scale representation and ordinal encoding may be useful to the research community, and also that our methodology will be applied to other widely used ordinal scales for improving validity of datasets and bettering the results of machine learning tasks.
Highlights
Despite the vagueness and uncertainty that is intrinsic in any medical act, interpretation and decision, still little research has focused on how to explicitly take this uncertainty into account
We explore an alternative approach, that is encoding ordinal values in terms of scalar values on a continuous 100-point scale, according to the fuzzy set representation constructed from the subjective perceptions of the corresponding level on that scale
This paper addresses the fuzzification of a common terminology, which is adopted by the Health Level 7 (HL7) framework in the digital health domain, that characterises health conditions, the appearance of medical signs and other expressions of medical relevance
Summary
Despite the vagueness and uncertainty that is intrinsic in any medical act, interpretation and decision (including acts of data reporting and representation of relevant medical conditions), still little research has focused on how to explicitly take this uncertainty into account. We focus on the representation of a general and wide-spread medical terminology, which is grounded on a traditional and well-established convention, to represent severity of health conditions (for instance, pain, visible signs), ranging from Absent to Extreme. In order to deal with the intrinsic uncertainty of the medical domain [4], a natural choice has always been to make use of fuzzy logic and fuzzy sets. Several surveys on this connection can be found in literature, for instance [5,6,7,8]. The recent work by Zywica [15] goes in this direction, by using fuzzy sets for transforming heterogenous data in homogenous ones and to deal with the lack of knowledge
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