Singular profile of diabetics

The therapeutic monitoring of patients at home produces a mass of data that requires new methods for analyzing and processing. The main challenge of medical data processing is the management of high intra-subject and inter-subject variabilities. The need for specific dashboards for both the patient and the group of patients with similar therapeutic behaviors is another difficulty. This paper describes a new way to analyze such medical data through the use of singular profiles of elderly patients in a population with type 2 diabetes. Our goal is to develop a methodology of data processing for following the insulin therapy at home. The first step of processing consists in the fuzzification of the attributes within the data samples to ensure the robustness of the method. The singularity index we propose assesses the fuzzy attributes relative to each patient. This index is obtained by computing the power of the fuzzy set associated with each attribute. The singularity of the attributes permits us to give the singular profile of each patient. The visualization step leads us to propose empirical rules to obtain three kinds of different profiles. This robust approach also permits us to highlight three clusters of elderly diabetics. The three clusters appear very similar as the ones obtained when using classical automated methods of clustering such as the k-medoids. By extending this approach, the ultimate goal of our future developments is the design of a recommender system for type 2 diabetics with insulin therapy.