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Developing an Individual Glucose Prediction Model Using Recurrent Neural Network

In this study, we propose a personalized glucose prediction model using deep learning for hospitalized patients who experience Type-2 diabetes. We aim for our model to assist the medical personnel who check the blood glucose and control the amount of insulin doses. Herein, we employed a deep learnin...

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Autores principales: Kim, Dae-Yeon, Choi, Dong-Sik, Kim, Jaeyun, Chun, Sung Wan, Gil, Hyo-Wook, Cho, Nam-Jun, Kang, Ah Reum, Woo, Jiyoung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7696446/
https://www.ncbi.nlm.nih.gov/pubmed/33198170
http://dx.doi.org/10.3390/s20226460
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author Kim, Dae-Yeon
Choi, Dong-Sik
Kim, Jaeyun
Chun, Sung Wan
Gil, Hyo-Wook
Cho, Nam-Jun
Kang, Ah Reum
Woo, Jiyoung
author_facet Kim, Dae-Yeon
Choi, Dong-Sik
Kim, Jaeyun
Chun, Sung Wan
Gil, Hyo-Wook
Cho, Nam-Jun
Kang, Ah Reum
Woo, Jiyoung
author_sort Kim, Dae-Yeon
collection PubMed
description In this study, we propose a personalized glucose prediction model using deep learning for hospitalized patients who experience Type-2 diabetes. We aim for our model to assist the medical personnel who check the blood glucose and control the amount of insulin doses. Herein, we employed a deep learning algorithm, especially a recurrent neural network (RNN), that consists of a sequence processing layer and a classification layer for the glucose prediction. We tested a simple RNN, gated recurrent unit (GRU), and long-short term memory (LSTM) and varied the architectures to determine the one with the best performance. For that, we collected data for a week using a continuous glucose monitoring device. Type-2 inpatients are usually experiencing bad health conditions and have a high variability of glucose level. However, there are few studies on the Type-2 glucose prediction model while many studies performed on Type-1 glucose prediction. This work has a contribution in that the proposed model exhibits a comparative performance to previous works on Type-1 patients. For 20 in-hospital patients, we achieved an average root mean squared error (RMSE) of 21.5 and an Mean absolute percentage error (MAPE) of 11.1%. The GRU with a single RNN layer and two dense layers was found to be sufficient to predict the glucose level. Moreover, to build a personalized model, at most, 50% of data are required for training.
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spelling pubmed-76964462020-11-29 Developing an Individual Glucose Prediction Model Using Recurrent Neural Network Kim, Dae-Yeon Choi, Dong-Sik Kim, Jaeyun Chun, Sung Wan Gil, Hyo-Wook Cho, Nam-Jun Kang, Ah Reum Woo, Jiyoung Sensors (Basel) Article In this study, we propose a personalized glucose prediction model using deep learning for hospitalized patients who experience Type-2 diabetes. We aim for our model to assist the medical personnel who check the blood glucose and control the amount of insulin doses. Herein, we employed a deep learning algorithm, especially a recurrent neural network (RNN), that consists of a sequence processing layer and a classification layer for the glucose prediction. We tested a simple RNN, gated recurrent unit (GRU), and long-short term memory (LSTM) and varied the architectures to determine the one with the best performance. For that, we collected data for a week using a continuous glucose monitoring device. Type-2 inpatients are usually experiencing bad health conditions and have a high variability of glucose level. However, there are few studies on the Type-2 glucose prediction model while many studies performed on Type-1 glucose prediction. This work has a contribution in that the proposed model exhibits a comparative performance to previous works on Type-1 patients. For 20 in-hospital patients, we achieved an average root mean squared error (RMSE) of 21.5 and an Mean absolute percentage error (MAPE) of 11.1%. The GRU with a single RNN layer and two dense layers was found to be sufficient to predict the glucose level. Moreover, to build a personalized model, at most, 50% of data are required for training. MDPI 2020-11-12 /pmc/articles/PMC7696446/ /pubmed/33198170 http://dx.doi.org/10.3390/s20226460 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kim, Dae-Yeon
Choi, Dong-Sik
Kim, Jaeyun
Chun, Sung Wan
Gil, Hyo-Wook
Cho, Nam-Jun
Kang, Ah Reum
Woo, Jiyoung
Developing an Individual Glucose Prediction Model Using Recurrent Neural Network
title Developing an Individual Glucose Prediction Model Using Recurrent Neural Network
title_full Developing an Individual Glucose Prediction Model Using Recurrent Neural Network
title_fullStr Developing an Individual Glucose Prediction Model Using Recurrent Neural Network
title_full_unstemmed Developing an Individual Glucose Prediction Model Using Recurrent Neural Network
title_short Developing an Individual Glucose Prediction Model Using Recurrent Neural Network
title_sort developing an individual glucose prediction model using recurrent neural network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7696446/
https://www.ncbi.nlm.nih.gov/pubmed/33198170
http://dx.doi.org/10.3390/s20226460
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