Mining Therapeutic Patterns from Clinical Data for Juvenile Diabetes

The disease of diabetes mellitus has spread in recent years across the world, and has thus become an even more important medical problem. Despite numerous solutions already proposed, the problem of management of glucose concentration in the blood of a diabetic patient still remains as a challenge and raises interest among researchers. The data-driven models of glucose-insulin interaction are one of the recent directions of research. In particular, a data-driven model can be constructed using the idea of sequential patterns as the knowledge representation method. In this paper a new hierarchical, template-based approach for mining sequential patterns is proposed. The paper proposes also to use functional abstractions for the representation and mining of clinical data. Due to the experts knowledge involved in the construction of functional abstractions and sequential templates, the discovered underlying template-based patters can be easily interpreted by physicians and are able to provide recommendations of medical therapy. The proposed methodology was validated by experiments using real clinical data of juvenile diabetes.