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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F19%3A43954250" target="_blank" >RIV/49777513:23520/19:43954250 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S1568494619300249" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1568494619300249</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.asoc.2019.01.020" target="_blank" >10.1016/j.asoc.2019.01.020</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/LO1506" target="_blank" >LO1506: Podpora udržitelnosti centra NTIS - Nové technologie pro informační společnost</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Applied Soft Computing Journal

ISSN

1568-4946

e-ISSN

—

Svazek periodika

77

Číslo periodika v rámci svazku

APR 2019

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

13

Strana od-do

316-328

Kód UT WoS článku

000462042200024

EID výsledku v databázi Scopus

2-s2.0-85061062477