%Aigaion2 BibTeX export from Knowledge Engineering Publications
%Saturday 18 December 2021 12:02:51 AM

@MASTERSTHESIS{,
    author = {Slawik, Albert},
     title = {Blood Glucose Prediction with Convolutional Neural Networks},
      type = {Masters Thesis},
      year = {2018},
    school = {TU Darmstadt},
  abstract = {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.}
}