Improved forecasting of blood glucose could aid in the management of diabetes. Recently proposed neural network architectures that use stacked fully connected layers with residual backcasting have achieved state-of-the-art performance on benchmark time-series forecasting tasks. Though promising, previous work ignores opportunities for additional supervision, and the use of fully connected layers fails to account for the temporal nature of the signal. Here, we propose a new architecture that builds on previous work by learning to forecast gradually in stages or blocks. Our updates include replacing the fully connected block structure with a recurrent neural network and adding additional losses to provide auxiliary supervision. In addition, we leverage important context in the form of additional input signals. Applied to the task of glucose forecasting, we find that each of these modifications offers an improvement in performance. On the task of predicting blood glucose values 30 minutes into the future, our proposed approach achieves a mean rMSE of 18.2 mg/dL, versus 21.2 mg/dL achieved by the baseline. These improvements get us closer to predictions that are reliable enough to be used in CGMs or insulin pumps to manage diabetes.

Deep Residual Time-Series Forecasting: Application to Blood Glucose Prediction

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.

Harry Rubin-Falcone

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Deep Residual Time-Series Forecasting: Application to Blood Glucose Prediction

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