TY - JOUR
T1 - Predicting Plasma Glucose From Interstitial Glucose Observations Using Bayesian Methods
AU - Hansen, Alexander Hildenbrand
AU - Duun-Henriksen, Anne Katrine
AU - Juhl, Rune
AU - Schmidt, Signe
AU - Nørgaard, Kirsten
AU - Jørgensen, John Bagterp
AU - Madsen, Henrik
PY - 2014
Y1 - 2014
N2 - One way of constructing a control algorithm for an artificial pancreas is to identify a model capable of predicting plasma glucose (PG) from interstitial glucose (IG) observations. Stochastic differential equations (SDEs) make it possible to account both for the unknown influence of the continuous glucose monitor (CGM) and for unknown physiological influences. Combined with prior knowledge about the measurement devices, this approach can be used to obtain a robust predictive model. A stochastic-differential-equation-based gray box (SDE-GB) model is formulated on the basis of an identifiable physiological model of the glucoregulatory system for type 1 diabetes mellitus (T1DM) patients. A Bayesian method is used to estimate robust parameters from clinical data. The models are then used to predict PG from IG observations from 2 separate study occasions on the same patient. First, all statistically significant diffusion terms of the model are identified using likelihood ratio tests, yielding inclusion of σIsc, σGp, and σGsc . Second, estimates using maximum likelihood are obtained, but prediction capability is poor. Finally a Bayesian method is implemented. Using this method the identified models are able to predict PG using only IG observations. These predictions are assessed visually. We are also able to validate these estimates on a separate data set from the same patient. This study shows that SDE-GBs and a Bayesian method can be used to identify a reliable model for prediction of PG using IG observations obtained with a CGM. The model could eventually be used in an artificial pancreas.
AB - One way of constructing a control algorithm for an artificial pancreas is to identify a model capable of predicting plasma glucose (PG) from interstitial glucose (IG) observations. Stochastic differential equations (SDEs) make it possible to account both for the unknown influence of the continuous glucose monitor (CGM) and for unknown physiological influences. Combined with prior knowledge about the measurement devices, this approach can be used to obtain a robust predictive model. A stochastic-differential-equation-based gray box (SDE-GB) model is formulated on the basis of an identifiable physiological model of the glucoregulatory system for type 1 diabetes mellitus (T1DM) patients. A Bayesian method is used to estimate robust parameters from clinical data. The models are then used to predict PG from IG observations from 2 separate study occasions on the same patient. First, all statistically significant diffusion terms of the model are identified using likelihood ratio tests, yielding inclusion of σIsc, σGp, and σGsc . Second, estimates using maximum likelihood are obtained, but prediction capability is poor. Finally a Bayesian method is implemented. Using this method the identified models are able to predict PG using only IG observations. These predictions are assessed visually. We are also able to validate these estimates on a separate data set from the same patient. This study shows that SDE-GBs and a Bayesian method can be used to identify a reliable model for prediction of PG using IG observations obtained with a CGM. The model could eventually be used in an artificial pancreas.
KW - Bayesian methods
KW - Plasma glucose dynamic
KW - PG-IG dynamic
KW - Stochastic differential equations
KW - Stochastic gray-box modeling
KW - Type 1 diabetes mellitus
U2 - 10.1177/1932296814523878
DO - 10.1177/1932296814523878
M3 - Journal article
C2 - 24876584
SN - 1932-2968
VL - 8
SP - 321
EP - 330
JO - Journal of Diabetes Science and Technology
JF - Journal of Diabetes Science and Technology
IS - 2
ER -