  Control of blood glucose is essential for diabetes management. Current
digital therapeutic approaches for subjects with Type 1 diabetes mellitus
(T1DM) such as the artificial pancreas and insulin bolus calculators leverage
machine learning techniques for predicting subcutaneous glucose for improved
control. Deep learning has recently been applied in healthcare and medical
research to achieve state-of-the-art results in a range of tasks including
disease diagnosis, and patient state prediction among others. In this work, we
present a deep learning model that is capable of forecasting glucose levels
with leading accuracy for simulated patient cases (RMSE = 9.38$\pm$0.71 [mg/dL]
over a 30-minute horizon, RMSE = 18.87$\pm$2.25 [mg/dL] over a 60-minute
horizon) and real patient cases (RMSE = 21.07$\pm$2.35 [mg/dL] for 30-minute,
RMSE = 33.27$\pm$4.79\% for 60-minute). In addition, the model provides
competitive performance in providing effective prediction horizon ($PH_{eff}$)
with minimal time lag both in a simulated patient dataset ($PH_{eff}$ =
29.0$\pm$0.7 for 30-min and $PH_{eff}$ = 49.8$\pm$2.9 for 60-min) and in a real
patient dataset ($PH_{eff}$ = 19.3$\pm$3.1 for 30-min and $PH_{eff}$ =
29.3$\pm$9.4 for 60-min). This approach is evaluated on a dataset of 10
simulated cases generated from the UVa/Padova simulator and a clinical dataset
of 10 real cases each containing glucose readings, insulin bolus, and meal
(carbohydrate) data. Performance of the recurrent convolutional neural network
is benchmarked against four algorithms. The proposed algorithm is implemented
on an Android mobile phone, with an execution time of $6$ms on a phone compared
to an execution time of $780$ms on a laptop.
