We consider the question of 30-minute prediction of blood glucose levels measured by continuous glucose monitoring devices, using clinical data. While most studies of this nature deal with one patient at a time, we take a certain percentage of patients in the data set as training data, and test on the remainder of the patients; i.e., the machine need not re-calibrate on the new patients in the data set. We demonstrate how deep learning can outperform shallow networks in this example. One novelty is to demonstrate how a parsimonious deep representation can be constructed using domain knowledge.
@article{arxiv.1707.05828,
title = {A deep learning approach to diabetic blood glucose prediction},
author = {H. N. Mhaskar and S. V. Pereverzyev and M. D. van der Walt},
journal= {arXiv preprint arXiv:1707.05828},
year = {2017}
}