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Model optimization and parameter estimation in a bi-hormonal model of glucose dynamics in type 1 diabetes mellitus patients

Vosman, M.J. (2019) Model optimization and parameter estimation in a bi-hormonal model of glucose dynamics in type 1 diabetes mellitus patients.

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Abstract:Modelling of the glucose metabolism in people with type 1 diabetes mellitus (T1DM) is relevant for the in silico testing of artificial pancreas (AP) systems and can be applied to predictive control in such systems. Problems with existing models arise when these models need to be identified based on limited available data. In this thesis, simplification of existing model structures was applied to solve this problem. Models of subcutaneous insulin and glucagon kinetics were identified for simulating insulin and glucagon plasma concentrations in an average patient with T1DM. For modelling of the glucose concentration in response to these hormonal concentrations, model structures of varying complexity were proposed and identified. Performance of these models to predict glucose levels in patients with T1DM up to three hours forward was determined. Results showed that prediction performance was best for a simple nonlinear model structure with only two subject-specific parameters. 3 hours forward, the median absolute error was smaller than 1 mmol/L. This study shows that parameters in simplified model structures can be estimated with the applied methods. Despite some limitations, the study demonstrates the potential of a simple compartmental model to be applied for improvement of glucose control in T1DM patients.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:30 exact sciences in general, 31 mathematics, 44 medicine, 50 technical science in general
Programme:Biomedical Engineering MSc (66226)
Link to this item:http://purl.utwente.nl/essays/79470
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