Exploring Heterogeneity with Category and Cluster Analyses for Mixed Data

Precision medicine aims to overcome the traditional one-model-fits-the-whole-population approach that is unable detect heterogeneous disease patterns and make accurate personalized predictions. Heterogeneity particularly relevant for patients with complications of type 2 diabetes, including diabetic kidney (DKD). We focus on a DKD longitudinal dataset, aiming find specific subgroups characteristics have close response therapeutic treatment. develop an based some particular concepts category theory cluster analysis explore individualized modelings achieving insights onto evolution. This paper exploits visualization tools provided by theory, bridges category-based abstract works real datasets. build deriving clusters at different time points, considering set variables characterizing state patients. analyze how affect progress, which drug combinations are more effective each The retrieved information can foster strategies