Identifying Differential Equations for the Prediction of Blood Glucose using Sparse Identification of Nonlinear Systems.

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Abstract

Describing dynamic medical systems using machine learning is a challenging topic with a wide range of applications. In this work, the possibility of modeling the blood glucose level of diabetic patients purely on the basis of measured data is described. A combination of the influencing variables insulin and calories are used to find an interpretable model. The absorption speed of external substances in the human body depends strongly on external influences, which is why time-shifts are added for the influencing variables. The focus is put on identifying the best time-shifts that provide robust models with good prediction accuracy that are independent of other unknown external influences. The modeling is based purely on the measured data using Sparse Identification of Nonlinear Dynamics. A differential equation is determined which, starting from an initial value, simulates blood glucose dynamics. By applying the best model to test data, we can show that it is possible to simulate the long-term blood glucose dynamics using differential equations and few, influencing variables.

Original languageEnglish
Title of host publicationComputer Aided Systems Theory – EUROCAST 2022 - 18th International Conference, Revised Selected Papers
EditorsRoberto Moreno-Díaz, Franz Pichler, Alexis Quesada-Arencibia
Pages181-188
Number of pages8
DOIs
Publication statusPublished - 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13789 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • Differential equations
  • Machine learning
  • Symbolic regression

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