@book{13615,
  keywords = {inverse problems, adaptive parameter choice, diabetes technology, DIAdvisor, learning theory, Meta-Learning, numerical analysis, numerical differentiation, prediction of the blood glucose concentration, Regularization methods},
  author = {Valeriya Naumova},
  title = {Numerical Methods for Diabetes Technology},
  abstract = {In this work, we develop new mathematical tools for diabetes therapy management, where the key problem is to predict the future blood glucose levels of a diabetic patient from available current and past information about therapeutically valuable factors. We provide a theoretical analysis of the developed techniques and demonstrate them in real-life applications. To show the efficiency of the developed mathematical tools, we provide an extensive collection of the results of numerical experiments with simulated and real clinical data, as well as comparing them with existing literature. This research has been performed in the course of the project \&$\#$39;DIAdvisor\&$\#$39; (DIAdvisor: personal glucose predictive diabetes advisor) funded by the European Commission within 7-th Framework Programme. The author gratefully acknowledges the support of the \&$\#$39;DIAdvisor\&$\#$39; consortium.},
  year = {2012},
  journal = {Mathematical Algorithms for a Better Management of Type 1 Diabetes},
  pages = {176},
  publisher = {LAP LAMBERT Academic Publishing},
  address = {Germany},
}