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.
@article{arxiv.2106.00884,
title = {Deep Personalized Glucose Level Forecasting Using Attention-based Recurrent Neural Networks},
author = {Mohammadreza Armandpour and Brian Kidd and Yu Du and Jianhua Z. Huang},
journal= {arXiv preprint arXiv:2106.00884},
year = {2021}
}