BiGRU-Att: Blood Glucose Prediction with Bidirectional Recurrent Neural Networks and Attention Mechanisms

This paper presents an advanced deep learning model that combines a bidirectional gated recurrent unit (BiGRU) and an attention mechanism for predicting blood glucose levels. The innovation of the model lies in its unique structural design, in which the BiGRU is able to capture the backward and forward dependencies in time series data, while the attention mechanism further enhances the sensitivity of the model to critical time steps, thus improving the accuracy of the prediction. This combination not only exploits the power of BiGRU in processing serial data, but also enables adaptive weighting of important features in the input data through the attention mechanism. We validate the effectiveness of the model through experiments on 10 in silicodatasets generated by the UVA/Padova T1D simulator. On these datasets, the model demonstrated excellent performance with a root mean square error (RMSE) of only 0.0719, a metric that is significantly lower than that of existing techniques, proving the superiority and innovativeness of our model for blood glucose prediction tasks.