A Grammatical Evolution Approach for Estimating Blood Glucose Levels

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11140%2F20%3A10425967" target="_blank" >RIV/00216208:11140/20:10425967 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00669806:_____/20:10425967 RIV/49777513:23520/20:43961697

Výsledek na webu

<a href="http://dx.doi.org/10.1109/GCWkshps50303.2020.9367402" target="_blank" >http://dx.doi.org/10.1109/GCWkshps50303.2020.9367402</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/GCWkshps50303.2020.9367402" target="_blank" >10.1109/GCWkshps50303.2020.9367402</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Grammatical Evolution Approach for Estimating Blood Glucose Levels

Popis výsledku v původním jazyce

The management of diabetes is a very complex task, hence devising automatic procedures able to predict the glycemic level can represent a significant step towards the building of an artificial pancreas capable of providing the needed amounts of insulin boluses.This paper presents a Grammatical Evolution-based algorithm aiming at extrapolating a regression model able to estimate the blood glucose level in future instants of time through interstitial glucose measurements. The hypothesis is that the amounts of carbohydrates assumed, of basal insulin levels and of those administered with boluses are known. Experiments, performed on a real-world database made up of five patients suffering from Type 1 diabetes, are shown in terms of Clark Error Grid analysis. To evaluate the effectiveness of the predictions derived from the proposed approach, the results obtained are compared against those obtained by other state-of-the-art evolutionary-based methods very recently proposed. (C) 2020 IEEE.

Název v anglickém jazyce

A Grammatical Evolution Approach for Estimating Blood Glucose Levels

Popis výsledku anglicky

The management of diabetes is a very complex task, hence devising automatic procedures able to predict the glycemic level can represent a significant step towards the building of an artificial pancreas capable of providing the needed amounts of insulin boluses.This paper presents a Grammatical Evolution-based algorithm aiming at extrapolating a regression model able to estimate the blood glucose level in future instants of time through interstitial glucose measurements. The hypothesis is that the amounts of carbohydrates assumed, of basal insulin levels and of those administered with boluses are known. Experiments, performed on a real-world database made up of five patients suffering from Type 1 diabetes, are shown in terms of Clark Error Grid analysis. To evaluate the effectiveness of the predictions derived from the proposed approach, the results obtained are compared against those obtained by other state-of-the-art evolutionary-based methods very recently proposed. (C) 2020 IEEE.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

30202 - Endocrinology and metabolism (including diabetes, hormones)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings

ISBN

978-1-72817-307-8

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

—

Název nakladatele

Institute of Electrical and Electronics Engineers

Místo vydání

New York

Místo konání akce

Taipei, Taiwan

Datum konání akce

7. 12. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—