Auto-Regressive Time Delayed jump neural network for blood glucose levels forecasting

Diabetes mellitus is a widespread chronic disease and is one of the main causes of death worldwide. In order to improve the quality of life of people with diabetes and reduce the occurrence of complications, it is fundamental to prevent glycemic levels from exceeding the physiologic range. With this purpose, many works in recent years have been developed to forecast future glycemic trends using machine learning algorithms that exploit the reading of continuous glucose monitoring sensors, which gather glycemic data from diabetic patients 24 h a day. However, their application is limited in practice by the fact that they usually require a large amount of training data and other heterogeneous features gathered from patients. For this reason, in this work we present a novel neural network capable of predicting future glycemic levels using only the past glucose values as input while needing a small amount of training data. The model is a jump neural network with the addition of feedback connections from the output to the hidden layer, and time delays for each of the input-to-hidden, output-to-hidden and input-to-output connections. Experiments were conducted on a private and a public dataset. We evaluated performance in terms of RMSE and of adverse event detection. The proposed model outperforms other methods suited for time series forecasting, as well as models for blood glucose level prediction present in the literature.