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

• URL: http://arxiv.org/abs/2106.00884v1
• Date: Wed, 2 Jun 2021 01:36:53 GMT
• Title: Deep Personalized Glucose Level Forecasting Using Attention-based Recurrent Neural Networks
• Authors: Mohammadreza Armandpour, Brian Kidd, Yu Du, Jianhua Z. Huang
• Abstract summary: We study the problem of blood glucose forecasting and provide a deep personalized solution. We analyze the data and detect important patterns. We empirically show the efficacy of our model on a real dataset.
• Score: 5.250950284616893
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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