LAUSR

This study proposes a graph-based method for representing the dynamics of chronic diabetes as a complex process with different characteristics. The study was based on the case histories of 6864 patients with diabetes mellitus, 90% of whom suffer from type 2 diabetes. Our method allows to predict the sequence of events during the development of type 2 diabetes for each patient. Typical developmental trajectories of the disease were investigated, their clustering was carried out, the trajectory patterns were identified and studied. Based on the constructed directed graph reflecting transitions between different conditions of the patients, the clustering of diabetic statuses was carried out using the Modularity Class method; 8 clusters were selected, each of them was interpreted and studied. The method of the disease developmental trajectories creation by means of machine learning methods was described. Unlike static models of a disease course, this method considers complete past information on the patient and his or her previous events, using each event of the course of disease to predict the next event.