A New Stochastic Approach for Modeling Glycemic Disturbances in Type 2 Diabetes

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

A New Stochastic Approach for Modeling Glycemic Disturbances in Type 2 Diabetes. / Clausen, Henrik; Knudsen, Torben; Al Ahdab, Mohamad; Aradottir, Tinna; Schmidt, Signe; Nørgaard, Kirsten; Leth, John.

In: I E E E Transactions on Biomedical Engineering, Vol. 68, No. 10, 2021, p. 3161-3172.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Clausen, H, Knudsen, T, Al Ahdab, M, Aradottir, T, Schmidt, S, Nørgaard, K & Leth, J 2021, 'A New Stochastic Approach for Modeling Glycemic Disturbances in Type 2 Diabetes', I E E E Transactions on Biomedical Engineering, vol. 68, no. 10, pp. 3161-3172. https://doi.org/10.1109/TBME.2021.3074868

APA

Clausen, H., Knudsen, T., Al Ahdab, M., Aradottir, T., Schmidt, S., Nørgaard, K., & Leth, J. (2021). A New Stochastic Approach for Modeling Glycemic Disturbances in Type 2 Diabetes. I E E E Transactions on Biomedical Engineering, 68(10), 3161-3172. https://doi.org/10.1109/TBME.2021.3074868

Vancouver

Clausen H, Knudsen T, Al Ahdab M, Aradottir T, Schmidt S, Nørgaard K et al. A New Stochastic Approach for Modeling Glycemic Disturbances in Type 2 Diabetes. I E E E Transactions on Biomedical Engineering. 2021;68(10):3161-3172. https://doi.org/10.1109/TBME.2021.3074868

Author

Clausen, Henrik ; Knudsen, Torben ; Al Ahdab, Mohamad ; Aradottir, Tinna ; Schmidt, Signe ; Nørgaard, Kirsten ; Leth, John. / A New Stochastic Approach for Modeling Glycemic Disturbances in Type 2 Diabetes. In: I E E E Transactions on Biomedical Engineering. 2021 ; Vol. 68, No. 10. pp. 3161-3172.

Bibtex

@article{30b769aca9e5440fb839febf36369538,
title = "A New Stochastic Approach for Modeling Glycemic Disturbances in Type 2 Diabetes",
abstract = "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.",
keywords = "Blood Glucose, Blood Glucose Self-Monitoring, Diabetes Mellitus, Type 2/drug therapy, Humans, Meals, Stochastic Processes",
author = "Henrik Clausen and Torben Knudsen and {Al Ahdab}, Mohamad and Tinna Aradottir and Signe Schmidt and Kirsten N{\o}rgaard and John Leth",
year = "2021",
doi = "10.1109/TBME.2021.3074868",
language = "English",
volume = "68",
pages = "3161--3172",
journal = "IEEE Transactions on Biomedical Engineering",
issn = "0018-9294",
publisher = "Institute of Electrical and Electronics Engineers",
number = "10",

}

RIS

TY - JOUR

T1 - A New Stochastic Approach for Modeling Glycemic Disturbances in Type 2 Diabetes

AU - Clausen, Henrik

AU - Knudsen, Torben

AU - Al Ahdab, Mohamad

AU - Aradottir, Tinna

AU - Schmidt, Signe

AU - Nørgaard, Kirsten

AU - Leth, John

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 - Blood Glucose

KW - Blood Glucose Self-Monitoring

KW - Diabetes Mellitus, Type 2/drug therapy

KW - Humans

KW - Meals

KW - Stochastic Processes

U2 - 10.1109/TBME.2021.3074868

DO - 10.1109/TBME.2021.3074868

M3 - Journal article

C2 - 33881986

VL - 68

SP - 3161

EP - 3172

JO - IEEE Transactions on Biomedical Engineering

JF - IEEE Transactions on Biomedical Engineering

SN - 0018-9294

IS - 10

ER -

ID: 290599374