Modelling the glucose metabolism with backpropagation through time trained Elman nets

Abstract

Type-I diabetes mellitus patients can not produce the hormone insulin endogenously. As this hormone is necessary to control the blood sugar level, which is raised by eating, insulin must be delivered exogeneously. Delivering insulin exogeneously demands correct dosage to avoid an extremely high or low blood glucose level. Most patients are not able to administer the adequate insulin dose because they are not able to predict the evolution of their own glucose level after a meal. Therefore, a model of the glucose metabolism is of crucial interest to help patients to determine correct insulin doses. These models shall be capable of predicting the course of the blood glucose level for a couple of hours with reasonable precision. In this paper a computer aided assistance system for diabetes patients running on a mobile handheld device is presented. This assistance system mainly consists of a model of the glucose metabolism, implemented by a modified Elman net. The training is performed through the BPTT algorithm where the training data were generated with an analytical non-linear glucose metabolism model that is quite realistic but cannot be adapted to every single patient. The glucose metabolism process is defined by two inputs, injected insulin and ingested glucose, and one output, namely the blood glucose. Due to the fact that metabolic processes in general have large time constants this process is characterized by the fact that the current net output, that is the blood glucose level, heavily depends on data that are not present in the current input layer any more. The Elman net's context-layer is capable of storing this information. Simulation results demonstrate that the output of this type of neural network closely follows the reference.