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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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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. |
format | Online Article Text |
id | pubmed-7696446 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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