Blood glucose (BG) prediction plays an important role in daily BG control. Accurate prediction of short-term glucose concentration can provide early warning for hyperglycemia and hypoglycemia events. This paper proposed a novel framework that combined an online prediction model with a residual compensation network. The autoregressive moving average (ARMA) model was used for online blood glucose prediction and the neural network was applied for compensation of prediction error. The advantages of this combined framework are: (1) the online ARMA model is efficient and robust to capture time-varying glucose dynamics, (2) the residual compensation network is capable to estimate errors from the online prediction model. The performance of this method was evaluated by the root mean squared error (RMSE) and the mean absolute error (MAE) in the dataset of OhioT1DM.The results were shown in detail that the mean values of the best RMSE of six patients at 30-min and 60-min horizon were 20.03 and 34.89 respectively, and the best MAE at 30-min and 60-min horizon were 14.52 and 24.61. Compared with the ARMA model, the combined predictor with a residual compensation network shows better prediction accuracy. Thus, we concluded that the proposed framework was an available approach for online blood glucose level prediction (BGLP).

Online Blood Glucose Prediction Using Autoregressive Moving Average Model with Residual Compensation Network

The 5th International Workshop on Knowledge Discovery in Healthcare Data (KDH 2020)

technical paper

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ECAI 2020

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

Ning Ma

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Online Blood Glucose Prediction Using Autoregressive Moving Average Model with Residual Compensation Network

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