Detection of Abnormalities in Type II Diabetic Patients Using Particle Filters

This study proposes a strategy for detecting possible dysfunction of organs such as the liver, pancreas, muscles and adipose tissues in a group of type II diabetic patients. Several in silico clinical trials are performed on a previously developed type II diabetes model. Since the pancreatic insulin secretion rate and glucose metabolic rates of different organs represent the functional behavior of the corresponding organs, calculated values of these rates are analyzed and compared with the corresponding rates calculated from a healthy subject's model to detect possible abnormalities. These rates are calculated from estimated values of glucose and insulin concentrations inside the corresponding organs/tissues from the physiological model. Estimation of the concentrations in body organs/tissues is carried out using a sequential Monte Carlo filtering method called particle filters. The results show that the proposed strategy is capable of detecting deficiencies in hepatic and peripheral glucose disposal, hepatic glucose production and pancreatic insulin secretion. The information provided by this strategy can potentially be used to tailor patient dietary requirements and/or select appropriate medications for the patients.