Controlling blood glucose level for patient with type 2 diabetes mellitus (T2DM) has been influenced by many variables with significant levels of variability, such as insulin sensitivity, carbohydrates intake, exercise, and more. These variabilities make controlling blood glucose level a complex problem. In patients with advanced T2DM, when the body fails to regulate blood glucose level, an external loop including an insulin pump and a glucose measurement device can be used in maintaining glucose regulation. However, the control of blood glucose level may fail even in patient with insulin pump therapy. Therefore, building a reliable model-based fault detection system to detect failures in controlling blood glucose level dealing with all variability, uncertainty, and lack of accuracy is critical. In this article, we propose utilizing a validated robust model-based fault detection technique based on a Sequential Monte Carlo (SMC) filtering method for detecting faults in the insulin infusion system and detecting patient's organ dysfunction. For detecting faults, a previously validated metabolic model of glucose regulation in T2DM is used. Our results show that the proposed technique is capable of detecting disconnection in insulin infusion systems and detecting peripheral and hepatic insulin resistance.
['Melissa Barazandegan', 'K.E. Kwok', 'R.B. Gopaluni']
Model-based detection of organ dysfunction and faults in insulin infusion devices for type 2 diabetic patients