A Bayesian network for modelling blood glucose concentration and exercise in type 1 diabetes.
This article presents a new statistical approach to analysing the effects of everyday physical activity on blood glucose concentration in people with type 1 diabetes. A physiologically based model of blood glucose dynamics is developed to cope with frequently sampled data on food, insulin and habitual physical activity; the model is then converted to a Bayesian network to account for measurement error and variability in the physiological processes. A simulation study is conducted to determine the feasibility of using Markov chain Monte Carlo methods for simultaneous estimation of all model parameters and prediction of blood glucose concentration. Although there are problems with parameter identification in a minority of cases, most parameters can be estimated without bias. Predictive performance is unaffected by parameter misspecification and is insensitive to misleading prior distributions. This article highlights important practical and theoretical issues not previously addressed in the quest for an artificial pancreas as treatment for type 1 diabetes. The proposed methods represent a new paradigm for analysis of deterministic mathematical models of blood glucose concentration.
Southampton Statistical Sciences Research Institute, University of Southampton, Southampton, UK sean.ewings@soton.ac.uk.
Sean M
SM
Ewings
Southampton Statistical Sciences Research Institute, University of Southampton, Southampton, UK.
Sujit K
SK
Sahu
Faculty of Engineering and the Environment, University of Southampton, Southampton, UK.
John J
JJ
Valletta
Faculty of Medicine, University of Southampton, Southampton, UK.
Christopher D
CD
Byrne
Faculty of Engineering and the Environment, University of Southampton, Southampton, UK.
Andrew J
AJ
Chipperfield
D001499:Bayes Theorem; D001786:Blood Glucose / Q000502:physiology*; D003922:Diabetes Mellitus, Type 1 / Q000097:blood / Q000628:therapy*; D015444:Exercise / Q000502:physiology; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method
D001786:Blood Glucose
Bayesian network; artificial pancreas; exercise; free-living data; physical activity energy expenditure; type 1 diabetes