Fuzzification of ordinal classes. The case of the HL7 severity grading

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. In this paper, we focus on a general and wide-spread HL7 terminology, which is grounded on a traditional and well-established convention, to represent severity of health conditions (e.g., pain, visible signs), ranging from absent to very severe (as a matter of fact, different versions of this standard present minor differences, like ‘minor’ instead of ‘mild’, or ‘fatal’ inst ead of ‘very severe’). Our aim is to provide a fuzzy version of this terminology. To this aim, we conducted a questionnaire-based qualitative research study involving a relatively large sample of clinicians to represent numerically the five different labels of the standard terminology: absent, mild, moderate, severe and very severe. Using the collected values we then present and discuss three different possible representations that address the vagueness of medical interpretation by taking into account the perceptions of domain experts. In perspective, our hope is to use the resulting fuzzifications to improve machine learning approaches to medicine.