TY  - RPRT
T1  - Blood Glucose Prediction with Convolutional Neural Networks
A1  - Slawik, Albert
Y1  - 2018
M1  - Masters Thesis
T2  - TU Darmstadt
N2  - Type 1 diabetes mellitus is a metabolic disorder in which the body’s own insulin regulation is
disturbed. To stay within healthy blood glucose levels, patients have to do this regulation themselves
by injecting doses of insulin. To prevent hyperglycemia as well as hypoglycemia, it is
helpful to know the further progression of blood glucose. Previous work did this successfully by
using support vector regression. Since convolutional neural networks recently achieved extraordinary
performance in time series regression tasks, it is reasonable to use them to forecast blood
glucose. The integration of discrete events measured by smart devices could further increase the
prediction quality. Therefore this work uses convolutional neural networks with shared weights
on historical data to predict the blood glucose levels. By this method, it was possible to achieve
a mean absolute error in prediction of 8.74 where currently sold solutions lead to an error of
10.1.
ER  -