Multi-Scale Long Short-Term Memory Network with Multi-Lag Structure for Blood Glucose Prediction

Abstract

Accurate blood glucose (BG) prediction is necessary for daily glucose management of diabetes therapy. As glucose dynamics are often affected by various factors, such as diet, physical exercise, and insulin injection, it is difficult to consider all the relevant information and make a balance between the high-dimensional inputs and learning efficiency for a deep learning network. In this work, a novel multivariate predictor with a multi-scale long short-term memory (MS-LSTM) network was developed to automatically characterize the high-dimensional temporal dynamics and extract the features of blood glucose fluctuation and temporal trends sufficiently. Meanwhile, a multi-lag structure is designed for multiple variables, which can extract the dependence between different variables and blood glucose fluctuations more effectively. Furthermore, long-term sparse information was encoded and compressed to improve the learning efficiency of this deep learning network. The predictive capability of the proposed method was illustrated through 30-min and 60-min ahead glucose prediction in the OhioT1DM-2 Dataset. The root means square error (RMSE) values of 30-min and 60-min ahead predictions were 19.048 and 32.029, respectively, and the mean absolute error (MAE) values of 30-min and 60-min ahead predictions were 13.503 and 23.833. The results demonstrate the efficiency and prediction accuracy of the offline deep learning network, especially in the case of high-dimensional variables availability.