A nonlinear blind identification approach to modeling of diabetic patients

Modeling, simulation and control have become effective tools for the treatment of type 1 diabetic patients in the last decades. The availability of reliable models able to predict and/or simulate the behavior of diabetic patients is thus fundamental in this context. Several models, based on first principles or black-box approaches, have been proposed to fulfill this need. However, a common problem to these approaches is that they are not able to recover or to systematically account for the various unmeasured signals which affect a diabetic patient (e.g. food, physical activity, emotions, etc.). In this paper, we propose a blind identification approach, which allows us to derive accurate models of type 1 diabetes patients and to efficiently recover the unmeasured input signals. A simulated example, regarding identification of the blood glucose concentration in type 1 diabetes patients, is presented to demonstrate the effectiveness of the proposed approac

Blood Glucose Forecasting using LSTM Variants under the Context of Open Source Artificial Pancreas System

High accuracy of blood glucose prediction over the long term is essential for preventative diabetes management. The emerging closed-loop insulin delivery system such as the artificial pancreas system (APS) provides opportunities for improved glycaemic control for patients with type 1 diabetes. Existing blood glucose studies are proven effective only within 30 minutes but the accuracy deteriorates drastically when the prediction horizon increases to 45 minutes and 60 minutes. Deep learning, especially for long short term memory (LSTM) and its variants have recently been applied in various areas to achieve state-of-the-art results in tasks with complex time series data. In this study, we present deep LSTM based models that are capable of forecasting long term blood glucose levels with improved prediction and clinical accuracy. We evaluate our approach using 20 cases(878,000 glucose values) from Open Source Artificial Pancreas System (OpenAPS). On 30-minutes and 45-minutes prediction, our Stacked-LSTM achieved the best performance with Root-Mean-Square-Error (RMSE) marks 11.96 & 15.81 and Clark-Grid-ZoneA marks 0.887 & 0.784. In terms of 60-minutes prediction, our ConvLSTM has the best performance with RMSE = 19.6 and Clark-Grid-ZoneA=0.714. Our models outperform existing methods in both prediction and clinical accuracy. This research can hopefully support patients with type 1 diabetes to better manage their behavior in a more preventative way and can be used in future real APS context