Insulin Dependent Diabetes Mellitus (IDDM) is a chronic disease characterized by the inability of the pancreas to produce sufficient amounts of insulin. Daily compensation of the deficiency requires 4-6 insulin injections to be taken daily, the aim of this insulin therapy being to maintain normoglycemia—i.e., a blood glucose level between 4-7 mmol/L. To determine the quantity and timing of these injections, various different approaches are used. Currently, mostly qualitative and semiquantitative models and reasoning are used to design such a therapy. Here, an attempt is made to show how system identification and control may be used to estimate predictive quantitative models to be used in design of optimal insulin regimens. The system was divided into three subsystems, the insulin subsystem, the glucose subsystem and the insulin-glucose interaction. The insulin subsystem aims to describe the absorbtion of injected insulin from the subcutaneous depots and the glucose subsystem the absorbtion of glucose from the gut following a meal. These subsystems were modeled using compartment models and proposed models found in the literature. Several black-box models and grey-box models describing the insulin-glucose interaction were developed and analysed. These models were fitted to real data monitored by an IDDM patient. Many difficulties were encountered, typical of biomedical systems: Non-uniform and scarce sampling, time-varying dynamics and severe nonlinearities were some of the difficulties encountered during the modeling. None of the proposed models were able to describe the system accurately in all aspects during all conditions. However, all the linear models shared some dynamics. Based on the estimated models, short-term blood glucose predictors for up to two-hour-ahead blood glucose prediction were investigated.

The DIAdvisor™ project is a recently finished EC/FP7 funded project aiming at the development of a blood Glucose prediction device using easily available information to optimize the therapy of patients with diabetes. The project achieved its objectives by developing a unique system for diabetic patients to help them manage their disease effectively, optimizing safety and giving an improved quality of life. One main long-term outcome of DIAdvisor was reduction the negative effects of long-term hyperglycemia with no increased occurrence of hypoglycemia. A comprehensive clinical database was established and utilized for systems development. In-silico testing showed promising results. In parallel a device platform was developed for running these systems and for further industrial development based on the project prototype. From 2010 a first prototype has been exposed to extensive in-vitro validation and is now undergoing clinical testing.