An evolutionary methodology for estimating blood glucose levels from interstitial glucose measurements and their derivatives

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@InProceedings{DeFalco:2018:ISCC,

• author = "I. {De Falco} and U. Scafuri and E. Tarantino and A. {Della Cioppa} and A. Giugliano and Tomas Koutny and Michal Krcma",
• booktitle = "2018 IEEE Symposium on Computers and Communications (ISCC)",
• title = "An evolutionary methodology for estimating blood glucose levels from interstitial glucose measurements and their derivatives",
• year = "2018",
• pages = "01158--01163",
• abstract = "The patients suffering from diabetes are subjected to several serious medical risks that can lead also to fatal consequences. To enhance the quality of life of these patients there is the necessity to devise an artificial pancreas able to inject an insulin bolus when needed. This paper presents a genetic-programming based algorithm to extrapolate a regression model able to estimate the blood glucose (BG) level through interstitial glucose (IG) measurements and their derivatives. This algorithm represents a possible step in building the fundamental element of such an artificial pancreas, namely a new evolutionary computation-based methodology to derive a mathematical relationship between BG and IG. The proposed evolutionary automatic procedure is evaluated on a real-world database made up of both BG and IG measurements of people suffering from Type 1 diabetes. The discovered model is validated through a comparison with other techniques during the experimental phase.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/ISCC.2018.8538682",
• ISSN = "1530-1346",
• month = jun,
• notes = "Also known as \cite{8538682}",

}

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

Citations