TY - GEN
T1 - Parameter Estimation for a Jump Diffusion Model of Type 2 Diabetic Patients in the Presence of Unannounced Meals
AU - Ahdab, Mohamad Al
AU - Papez, Milan
AU - Knudsen, Torben
AU - Aradottir, Tinna Bjork
AU - Schmidt, Signe
AU - Norgaard, Kirsten
AU - Leth, John
N1 - Funding Information:
*This work was funded by the IFD Grand Solution project ADAPT-T2D, project number 9068-00056B.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Type 2 diabetes (T2D) has become one of the most often encountered metabolic disorders threatening the human health. Unannounced meal intake and irregular physical activity cause abrupt changes in the blood glucose concentrations. Therefore, a reliable and accurate algorithms that account for these sudden concentration changes constitute a crucial part of automated insulin pumps and dose guiders. To this end, we develop a stochastic jump diffusion model for T2D patients, reflecting the irregular frequency and uncertain amount of consumed carbohydrates. Moreover, we design a method - ombining particle Markov chain Monte Carlo and particle learning - to estimate the unknown parameters of this model, considering only continuous glucose monitoring data and amounts of injected insulin. Our approach is verified on synthetic and clinical data, demonstrating its ability to estimate the unknown parameters with a varying degree of accuracy.
AB - Type 2 diabetes (T2D) has become one of the most often encountered metabolic disorders threatening the human health. Unannounced meal intake and irregular physical activity cause abrupt changes in the blood glucose concentrations. Therefore, a reliable and accurate algorithms that account for these sudden concentration changes constitute a crucial part of automated insulin pumps and dose guiders. To this end, we develop a stochastic jump diffusion model for T2D patients, reflecting the irregular frequency and uncertain amount of consumed carbohydrates. Moreover, we design a method - ombining particle Markov chain Monte Carlo and particle learning - to estimate the unknown parameters of this model, considering only continuous glucose monitoring data and amounts of injected insulin. Our approach is verified on synthetic and clinical data, demonstrating its ability to estimate the unknown parameters with a varying degree of accuracy.
UR - http://www.scopus.com/inward/record.url?scp=85124790587&partnerID=8YFLogxK
U2 - 10.1109/CCTA48906.2021.9658718
DO - 10.1109/CCTA48906.2021.9658718
M3 - Article in proceedings
AN - SCOPUS:85124790587
T3 - CCTA 2021 - 5th IEEE Conference on Control Technology and Applications
SP - 176
EP - 183
BT - CCTA 2021 - 5th IEEE Conference on Control Technology and Applications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE Conference on Control Technology and Applications, CCTA 2021
Y2 - 8 August 2021 through 11 August 2021
ER -