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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Veröffentlicht in:Sensors (Basel, Switzerland) Switzerland), 2020-11, Vol.20 (22), p.6460
Hauptverfasser: Kim, Dae-Yeon, Choi, Dong-Sik, Kim, Jaeyun, Chun, Sung Wan, Gil, Hyo-Wook, Cho, Nam-Jun, Kang, Ah Reum, Woo, Jiyoung
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Sprache:eng
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Zusammenfassung: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s20226460