BibTex format
@inproceedings{Chen:2018,
author = {Chen, J and Li, K and Herrero, P and Zhu, T and Georgiou, P},
pages = {69--73},
title = {Dilated recurrent neural network for short-time prediction of glucose concentration},
year = {2018}
}
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TY - CPAPER
AB - Diabetes is one of the diseases affecting 415 million people in the world. Developing a robust blood glucose (BG) prediction model has a profound influence especially important for the diabetes management. Subjects with diabetes need to adjust insulin doses according to the blood glucose levels to maintain blood glucose in a target range. An accurate glucose level prediction is able to provide subjects with diabetes with the future glucose levels, so that proper actions could be taken to avoid short-term dangerous consequences or long-term complications. With the developing of continuous glucose monitoring (CGM) systems, the accuracy of predicting the glucose levels can be improved using the machine learning techniques. In this paper, a new deep learning technique, which is based on the Dilated Recurrent Neural Network (DRNN) model, is proposed to predict the future glucose levels for prediction horizon (PH) of 30 minutes. And the method also can be implemented in real-time prediction as well. The result reveals that using the dilated connection in the RNN network, it can improve the accuracy of short-time glucose predictions significantly (RMSE = 19.04 in the blood glucose level prediction (BGLP) on and only on all data points provided).
AU - Chen,J
AU - Li,K
AU - Herrero,P
AU - Zhu,T
AU - Georgiou,P
EP - 73
PY - 2018///
SN - 1613-0073
SP - 69
TI - Dilated recurrent neural network for short-time prediction of glucose concentration
ER -