Convolutional Recurrent Neural Networks for Blood Glucose Prediction
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The main purpose of the artificial pancreas (AP) or any diabetes therapy for subjects with type 1 diabetes (T1D) is to maintain the subjects' plasma glucose level within the euglycemic range, which means below the threshold of hyperglycemia and above the threshold of hypoglycemia. The development of modern continuous glucose monitor (CGM) makes this feasible. Its continuous monitoring enables people to take actions before the hypo/hyperglycemia episodes. For this reason, an accurate blood glucose (BG) prediction is essential. It raises alarms before the real hypo/hyperglycemia scenarios and stays silent for non-hypo/non-hyperglycemia episodes. Data driven approaches have recently been widely used in statistical modelling in healthcare and medical researches, in particular deep neural network techniques. In this paper, the glucose prediction is seen as a probabilistically generative problem, and a hybrid deep neural network is proposed to combine the advantages of convolutional neural networks (CNN) and recurrent neural networks (RNN). Specifically, a multi-layer CNN is implemented as a feature extraction component, followed by a multi-layer modified long short term memory (LSTM) model to capture the probabilistic correlations between the future BG and historical BG level, meal information and insulin. The model is adaptive for the individual subject with T1D, and dropout layers are leveraged to avoid overfitting. The model is easily implemented using Tensorflow, and can be embedded to portable devices with limited computation resources. Experiments verify and evaluate the effectiveness of the proposed method using the simulated and clinical data.
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