TY - JOUR
T1 - A New Stochastic Approach for Modeling Glycemic Disturbances in Type 2 Diabetes
AU - Clausen, Henrik Glavind
AU - Knudsen, Torben
AU - Ahdab, Mohamad Al
AU - Björk Araddóttir, Tinna
AU - Schmidt, Signe
AU - Nørgaard, Kirsten
AU - Leth, John-Josef
PY - 2021
Y1 - 2021
N2 - Objective: To improve insulin treatment in type 2 diabetes (T2D) using model-based control techniques, the underlying model needs to be individualized to each patient. Due to the impact of unknown meals, exercise and other factors on the blood glucose, it is difficult to utilize available data from continuous glucose monitors (CGMs) for model fitting and parameter estimation purposes. Methods: To overcome this problem, we propose a novel method for modeling the glycemic disturbances as a stochastic process. To differentiate meals from other glycemic disturbances, we model the meal intake as a separate stochastic process while encompassing all other disturbances in another stochastic process. Using particle filtering, we validate the model on simulations as well as on clinical data. Results: Based on simulated CGM data, the residuals generated by the particle filter are white, indicating a good model fit. For the clinical data, we use parameter values estimated based on fasting glucose data. The residuals obtained from clinical CGM data contain correlations up to lag 5. Conclusion: The proposed model is shown to adequately describe the meal-induced glucose fluctuations in simulated CGM data while validations on clinical CGM data show promising results as well. Significance: The proposed model may lay the grounds for new ways of utilizing available CGM data, including CGM-based parameter estimation and stochastic optimal control.
AB - Objective: To improve insulin treatment in type 2 diabetes (T2D) using model-based control techniques, the underlying model needs to be individualized to each patient. Due to the impact of unknown meals, exercise and other factors on the blood glucose, it is difficult to utilize available data from continuous glucose monitors (CGMs) for model fitting and parameter estimation purposes. Methods: To overcome this problem, we propose a novel method for modeling the glycemic disturbances as a stochastic process. To differentiate meals from other glycemic disturbances, we model the meal intake as a separate stochastic process while encompassing all other disturbances in another stochastic process. Using particle filtering, we validate the model on simulations as well as on clinical data. Results: Based on simulated CGM data, the residuals generated by the particle filter are white, indicating a good model fit. For the clinical data, we use parameter values estimated based on fasting glucose data. The residuals obtained from clinical CGM data contain correlations up to lag 5. Conclusion: The proposed model is shown to adequately describe the meal-induced glucose fluctuations in simulated CGM data while validations on clinical CGM data show promising results as well. Significance: The proposed model may lay the grounds for new ways of utilizing available CGM data, including CGM-based parameter estimation and stochastic optimal control.
KW - Biological system modeling
KW - Blood
KW - Data models
KW - Diabetes
KW - Glucose
KW - Insulin
KW - Stochastic processes
KW - parameter estimation
KW - particle filtering
KW - stochastic differential equations
KW - type 2 diabetes
UR - http://www.scopus.com/inward/record.url?scp=85104649845&partnerID=8YFLogxK
U2 - 10.1109/TBME.2021.3074868
DO - 10.1109/TBME.2021.3074868
M3 - Journal article
SN - 0018-9294
VL - 68
SP - 3161
EP - 3172
JO - I E E E Transactions on Biomedical Engineering
JF - I E E E Transactions on Biomedical Engineering
IS - 10
ER -