{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T06:42:08Z","timestamp":1781592128508,"version":"3.54.5"},"reference-count":32,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2020,11,12]],"date-time":"2020-11-12T00:00:00Z","timestamp":1605139200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Bio &amp; Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT)","award":["2019M3E5D1A02069069"],"award-info":[{"award-number":["2019M3E5D1A02069069"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s20226460","type":"journal-article","created":{"date-parts":[[2020,11,12]],"date-time":"2020-11-12T10:00:32Z","timestamp":1605175232000},"page":"6460","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Developing an Individual Glucose Prediction Model Using Recurrent Neural Network"],"prefix":"10.3390","volume":"20","author":[{"given":"Dae-Yeon","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan 31151, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0932-9112","authenticated-orcid":false,"given":"Dong-Sik","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Medical Science, Soonchunhyang University, Asan 31538, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaeyun","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Big Data Engineering, Soonchunhyang University, Asan 31538, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sung Wan","family":"Chun","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan 31151, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyo-Wook","family":"Gil","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan 31151, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nam-Jun","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan 31151, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0732-5313","authenticated-orcid":false,"given":"Ah Reum","family":"Kang","sequence":"additional","affiliation":[{"name":"SCH Convergence Science Institute, Soonchunhyang University, Asan 31538, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8231-0018","authenticated-orcid":false,"given":"Jiyoung","family":"Woo","sequence":"additional","affiliation":[{"name":"Department of Big Data Engineering, Soonchunhyang University, Asan 31538, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1134","DOI":"10.1161\/01.CIR.100.10.1134","article-title":"Diabetes and Cardiovascular Disease","volume":"100","author":"Grundy","year":"1999","journal-title":"Circulation"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1016\/S2213-8587(17)30097-9","article-title":"The global economic burden of diabetes in adults aged 20\u201379 years: A cost-of-illness study","volume":"5","author":"Bommer","year":"2017","journal-title":"Lancet Diabetes Endocrinol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"American Diabetes Association (2018). 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