A fast generalizable solution method for glucose control algorithms (2010)
Type of ContentJournal Article
PublisherUniversity of Canterbury. Electrical and Computer Engineering
University of Canterbury. Mechanical Engineering
In critical care tight control of blood glucose levels has been shown to lead to better clinical outcomes. The need to develop new protocols for tight glucose control, as well as the opportunity to optimize a variety of other drug therapies, has led to resurgence in model-based medical decision support in this area. One still valid hindrance to developing new model-based protocols using so-called virtual patients, retrospective clinical data, and Monte Carlo methods is the large amount of computational time and resources needed. This paper develops fast analytical-based methods for insulin–glucose system model that are generalizable to other similar systems. Exploiting the structure and partial solutions in a subset of the model is the key in finding accurate fast solutions to the full model. This approach successfully reduced computing time by factors of 5600–144000 depending on the numerical error management method, for large (50– 164 patients) virtual trials and Monte Carlo analysis. It thus allows new model-based or model-derived protocols to be rapidly developed via extensive simulation. The new method is rigorously compared to existing standard numerical solutions and is found to be highly accurate to within 0.2%.
CitationHann, C.E., Docherty, P., Chase, J.G., Shaw, G.M. (2010) A fast generalizable solution method for glucose control algorithms. Mathematical Biosciences, 227(1), pp. 44-55.
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Keywordsmodel-based therapeutics; fast simulation; blood glucose control; Monte Carlo analysis; analytical solutions; computation
ANZSRC Fields of Research09 - Engineering::0903 - Biomedical Engineering
09 - Engineering::0906 - Electrical and Electronic Engineering
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