Developing an Individual Glucose Prediction Model Using Recurrent Neural Network

被引:7
|
作者
Kim, Dae-Yeon [1 ]
Choi, Dong-Sik [2 ]
Kim, Jaeyun [3 ]
Chun, Sung Wan [1 ]
Gil, Hyo-Wook [1 ]
Cho, Nam-Jun [1 ]
Kang, Ah Reum [4 ]
Woo, Jiyoung [3 ]
机构
[1] Soonchunhyang Univ, Dept Internal Med, Cheonan Hosp, Cheonan 31151, South Korea
[2] Soonchunhyang Univ, Dept Med Sci, Asan 31538, South Korea
[3] Soonchunhyang Univ, Dept Big Data Engn, Asan 31538, South Korea
[4] Soonchunhyang Univ, SCH Convergence Sci Inst, Asan 31538, South Korea
基金
新加坡国家研究基金会;
关键词
continuous glucose monitoring; diabetic inpatient; glucose prediction model; deep learning; HYPERGLYCEMIA; MANAGEMENT; TIME;
D O I
10.3390/s20226460
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
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.
引用
收藏
页码:1 / 15
页数:15
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