Deep Personalized Glucose Level Forecasting Using Attention-based Recurrent Neural Networks

TL;DR

This paper introduces a personalized deep learning model with attention mechanisms for accurate blood glucose forecasting in diabetics, improving long-term dependency learning and robustness over existing methods.

Contribution

It proposes a novel attention-based recurrent neural network that learns personalized and global models, with a new training procedure for time series data.

Findings

Model outperforms existing methods on real data

Effective in capturing long-term dependencies

Provides personalized predictions for each patient

Abstract

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.