  In this paper, we study the problem of blood glucose forecasting and provide
a deep personalized solution. Predicting blood glucose level in people with
diabetes has significant value because health complications of abnormal glucose
level are serious, sometimes even leading to death. Therefore, having a model
that can accurately and quickly warn patients of potential problems is
essential. To develop a better deep model for blood glucose forecasting, we
analyze the data and detect important patterns. These observations helped us to
propose a method that has several key advantages over existing methods: 1- it
learns a personalized model for each patient as well as a global model; 2- it
uses an attention mechanism and extracted time features to better learn
long-term dependencies in the data; 3- it introduces a new, robust training
procedure for time series data. We empirically show the efficacy of our model
on a real dataset.
