A Deep Learning Approach to Diabetic Blood Glucose Prediction

Frontiers in Applied Mathematics and Statistics

van der Walt, Maria D.¹  Mhaskar, Hrushikesh N.²  Pereverzyev, Sergei V.³ 

[1] Department of Mathematics, Vanderbilt University, Nashville, TN, United States;Institute of Mathematical Sciences, Claremont Graduate University, Claremont, CA, United States;Johann Radon Institute, Linz, Austria

关键词: deep learning;    Diffusion geometry;    Continuous glucose monitoring;    Blood glucose prediction;    Deep neural network;   

DOI  :  10.3389/fams.2017.00014

学科分类：数学（综合）

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

CC BY