A genetic programming-based regression for extrapolating a blood glucose-dynamics model from interstitial glucose measurements and their first derivatives

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@Article{DEFALCO:2019:ASC,

• author = "I. {De Falco} and A. {Della Cioppa} and A. Giugliano and A. Marcelli and Tomas Koutny and Michal Krcma and Umberto Scafuri and E. Tarantino",
• title = "A genetic programming-based regression for extrapolating a blood glucose-dynamics model from interstitial glucose measurements and their first derivatives",
• journal = "Applied Soft Computing",
• volume = "77",
• pages = "316--328",
• year = "2019",
• keywords = "genetic algorithms, genetic programming, Blood glucose estimation, Interstitial glucose, Regression models",
• ISSN = "1568-4946",
• DOI = "doi:10.1016/j.asoc.2019.01.020",
• URL = "http://www.sciencedirect.com/science/article/pii/S1568494619300249",
• abstract = "This paper illustrates the development and the applicability of an Evolutionary Computation approach to enhance the treatment of Type-1 diabetic patients that necessitate insulin injections. In fact, being such a disease associated to a malfunctioning pancreas that generates an insufficient amount of insulin, a way to enhance the quality of life of these patients is to implement an artificial pancreas able to artificially regulate the insulin dosage. This work aims at extrapolating a regression model, capable of estimating the blood glucose (BG) through interstitial glucose (IG) measurements and their numerical first derivatives. Such an approach represents a viable preliminary stage in building the basic component of this artificial pancreas. In particular, considered the high complexity of the reciprocal interactions, an evolutionary-based strategy is outlined to extrapolate a mathematical relationship between BG and IG and its derivative. The investigation is carried out about the accuracy of personalized models and of a global relationship model for all of the subjects under examination. The discovered models are assessed through a comparison with other models during the experiments on personalized and global data",

}

Genetic Programming entries for Ivanoe De Falco Antonio Della Cioppa A Giugliano Angelo Marcelli Tomas Koutny Michal Krcma Umberto Scafuri Ernesto Tarantino

Citations