TY  - JOUR
T1  - Prediction of Blood Glucose in Diabetic Inpatients Using LSTM Neural Network
AU  - Kim, Sang Hyeon 
AU  - Lee, Han Beom 
AU  - Jeon, Seong Wan 
AU  - Kim, Dae Yeon 
AU  - Lee, Sang Jeong 
JO  - Journal of KIISE, JOK
PY  - 2020
DA  - 2020/1/14
DO  - 10.5626/JOK.2020.47.12.1120
KW  - diabetes
KW  - blood glucose prediction
KW  - deep-learning
AB  - Diabetes is a chronic disease that causes serious complications, and at the medical site, doctors predict future changes in blood glucose based on patients past blood glucose trends and implement medical treatment. Recently, a CGM(Continuous Glucose Monitoring) measuring device has been introduced that can automatically measure blood glucose every five minutes to monitor continuous changes in blood glucose, and it is widely used in clinical applications. Based on the results of CGM blood glucose, the doctors predict and treat the timing of insulin administration and high risk of diabetes patients. In this paper, the blood glucose prediction model based on deep learning neural network is proposed. The proposed model is designed with an LSTM (Long Short-Term Memory) based neural network. It is designed to take historical blood glucose data as well as variables such as HbA1c(glycated hemoglobin) and BMI(body mass index). It was applied and tested using CGM blood glucose data from Type 2 Diabetes inpatients at a university hospital. The proposed model which patient characteristics show50% improvement at maximum in blood glucose prediction accuracy over the LSTM model of previous study.