Convolutional recurrent neural networks for glucose prediction

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Title: Convolutional recurrent neural networks for glucose prediction
Authors: Li, K
Daniels, J
Liu, C
Herrero-Vinas, P
Georgiou, P
Item Type: Journal Article
Abstract: Control of blood glucose is essential for diabetes management. Current digital therapeutic approaches for subjects with Type 1 diabetes mellitus (T1DM) such as the artificial pancreas and insulin bolus calculators leverage machine learning techniques for predicting subcutaneous glucose for improved control. Deep learning has recently been applied in healthcare and medical research to achieve state-of-the-art results in a range of tasks including disease diagnosis, and patient state prediction among others. In this work, we present a deep learning model that is capable of forecasting glucose levels with leading accuracy for simulated patient cases (RMSE = 9.38±0.71 [mg/dL] over a 30-minute horizon, RMSE = 18.87±2.25 [mg/dL] over a 60-minute horizon) and real patient cases (RMSE = 21.07±2.35 [mg/dL] for 30-minute, RMSE = 33.27±4.79\% for 60-minute). In addition, the model provides competitive performance in providing effective prediction horizon ( PHeff) with minimal time lag both in a simulated patient dataset ( PHeff = 29.0±0.7 for 30-min and PHeff = 49.8±2.9 for 60-min) and in a real patient dataset ( PHeff = 19.3±3.1 for 30-min and PHeff = 29.3±9.4 for 60-min). This approach is evaluated on a dataset of 10 simulated cases generated from the UVa/Padova simulator and a clinical dataset of 10 real cases each containing glucose readings, insulin bolus, and meal (carbohydrate) data. Performance of the recurrent convolutional neural network is benchmarked against four algorithms. The proposed algorithm is implemented on an Android mobile phone, with an execution time of 6ms on a phone compared to an execution time of 780ms on a laptop.
Issue Date: 1-Feb-2020
Date of Acceptance: 1-Apr-2019
DOI: 10.1109/JBHI.2019.2908488
ISSN: 2168-2194
Publisher: Institute of Electrical and Electronics Engineers
Start Page: 603
End Page: 613
Journal / Book Title: IEEE Journal of Biomedical and Health Informatics
Volume: 24
Issue: 2
Copyright Statement: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
Funder's Grant Number: EP/P00993X/1
Keywords: cs.CV
Publication Status: Published
Conference Place: United States
Online Publication Date: 2019-04-01
Appears in Collections:Faculty of Engineering
Electrical and Electronic Engineering

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