A Bayesian network for modelling blood glucose concentration and exercise in type 1 diabetes
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.
artificial pancreas, bayesian network, exercise, free-living data, physical activity energy, expenditure, type 1 diabetes
342-372
Ewings, Sean M.
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Sahu, Sujit K.
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Valletta, John J.
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Byrne, Christopher D.
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Chipperfield, Andrew J.
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June 2015
Ewings, Sean M.
d35d8c8e-7fef-4bb8-a35e-21d44b59232e
Sahu, Sujit K.
33f1386d-6d73-4b60-a796-d626721f72bf
Valletta, John J.
1d02219c-8adf-43ce-a00d-3c19e533a65c
Byrne, Christopher D.
1370b997-cead-4229-83a7-53301ed2a43c
Chipperfield, Andrew J.
524269cd-5f30-4356-92d4-891c14c09340
Ewings, Sean M., Sahu, Sujit K., Valletta, John J., Byrne, Christopher D. and Chipperfield, Andrew J.
(2015)
A Bayesian network for modelling blood glucose concentration and exercise in type 1 diabetes.
Statistical Methods in Medical Research, 24 (3), .
(doi:10.1177/0962280214520732).
(PMID:24492795)
Abstract
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.
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e-pub ahead of print date: 2 February 2014
Published date: June 2015
Keywords:
artificial pancreas, bayesian network, exercise, free-living data, physical activity energy, expenditure, type 1 diabetes
Organisations:
Primary Care & Population Sciences
Identifiers
Local EPrints ID: 363402
URI: http://eprints.soton.ac.uk/id/eprint/363402
ISSN: 0962-2802
PURE UUID: 98571c1a-e9c4-468e-b763-3f5965764a34
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Date deposited: 24 Mar 2014 16:49
Last modified: 15 Mar 2024 03:15
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Author:
Sean M. Ewings
Author:
John J. Valletta
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