TY - JOUR
T1 - An OGI model for personalized estimation of glucose and insulin concentration in plasma
AU - Wang, Weijie
AU - Wang, Shaoping
AU - Geng, Yixuan
AU - Qiao, Yajing
AU - Wu, Teresa
N1 - Funding Information:
The authors acknowledge funding support from Beijing Advanced Innovation Center for Big Data-based Precision Medicine. Authors also recognize support by Department of Endocrinology, Xuanwu Hospital Capital Medical University, China, for clinical suggestions, and efforts by Department of Endocrinology, The First Peoples Hospital of Yangquan City, China for recruitment of subjects and collection of physiological data.
Publisher Copyright:
© 2021 American Institute of Mathematical Sciences. All rights reserved.
PY - 2021
Y1 - 2021
N2 - Plasma glucose concentration (PGC) and plasma insulin concentration (PIC) are two essential metrics for diabetic regulation, but difficult to be measured directly. Often, PGC and PIC are estimated from continuous glucose monitoring and insulin delivery data. Nevertheless, the inter-individual variability and external disturbance (e.g. carbohydrate intake) bring challenges for accurate estimations. This study is to estimate PGC and PIC adaptively by identifying personalized parameters and external disturbances. An observable glucose-insulin (OGI) dynamic model is established to describe insulin absorption, glucose regulation, and glucose transport. The model parameters and disturbances can be extended to observable state variables and be identified dynamically by Bayesian filtering estimators. Two basic Gaussian noise based Bayesian filtering estimators, extended Kalman filtering (EKF) and unscented Kalman filtering (UKF), are implemented. Recognizing the prevalence of non-Gaussian noise, in this study, two new filtering estimators: particle filtering with Gaussian noise (PFG), and particle filtering with mixed non-Gaussian noise (PFM) are designed and implemented. The proposed OGI model in conjunction with the estimators is evaluated using the data from 30 in-silico subjects and 10 human participants. For in-silico subjects, the OGI with PFM estimator has the ability to estimate PIC and PGC adaptively, achieving RMSE of PIC 9.49 ± 3.81 mU/L, and PGC 0.89 ± 0.19 mmol/L. For human, the OGI with PFM has the promise to identify disturbances (95.46% ± 0.65% accurate rate of meal identification). OGI model provides a way to fully personalize the parameters and external disturbances in real time, and has potential clinical utility for artificial pancreas.
AB - Plasma glucose concentration (PGC) and plasma insulin concentration (PIC) are two essential metrics for diabetic regulation, but difficult to be measured directly. Often, PGC and PIC are estimated from continuous glucose monitoring and insulin delivery data. Nevertheless, the inter-individual variability and external disturbance (e.g. carbohydrate intake) bring challenges for accurate estimations. This study is to estimate PGC and PIC adaptively by identifying personalized parameters and external disturbances. An observable glucose-insulin (OGI) dynamic model is established to describe insulin absorption, glucose regulation, and glucose transport. The model parameters and disturbances can be extended to observable state variables and be identified dynamically by Bayesian filtering estimators. Two basic Gaussian noise based Bayesian filtering estimators, extended Kalman filtering (EKF) and unscented Kalman filtering (UKF), are implemented. Recognizing the prevalence of non-Gaussian noise, in this study, two new filtering estimators: particle filtering with Gaussian noise (PFG), and particle filtering with mixed non-Gaussian noise (PFM) are designed and implemented. The proposed OGI model in conjunction with the estimators is evaluated using the data from 30 in-silico subjects and 10 human participants. For in-silico subjects, the OGI with PFM estimator has the ability to estimate PIC and PGC adaptively, achieving RMSE of PIC 9.49 ± 3.81 mU/L, and PGC 0.89 ± 0.19 mmol/L. For human, the OGI with PFM has the promise to identify disturbances (95.46% ± 0.65% accurate rate of meal identification). OGI model provides a way to fully personalize the parameters and external disturbances in real time, and has potential clinical utility for artificial pancreas.
KW - Artificial pancreas
KW - Bayesian filter
KW - Model personalization
KW - Plasma glucose concentration
KW - Plasma insulin concentration
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U2 - 10.3934/mbe.2021420
DO - 10.3934/mbe.2021420
M3 - Article
C2 - 34814309
AN - SCOPUS:85118140537
VL - 18
SP - 8499
EP - 8523
JO - Mathematical Biosciences and Engineering
JF - Mathematical Biosciences and Engineering
SN - 1547-1063
IS - 6
ER -