De–randomized Meta-Differential Evolution for Calculating and Predicting Glucose Levels

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F19%3A43956220" target="_blank" >RIV/49777513:23520/19:43956220 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11140/19:10402654

Výsledek na webu

<a href="http://dx.doi.org/10.1109/CBMS.2019.00064" target="_blank" >http://dx.doi.org/10.1109/CBMS.2019.00064</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CBMS.2019.00064" target="_blank" >10.1109/CBMS.2019.00064</a>

Jazyk výsledku

angličtina

Název v původním jazyce

De–randomized Meta-Differential Evolution for Calculating and Predicting Glucose Levels

Popis výsledku v původním jazyce

A physiological model improves delivered healthcare, when constructing a medical device. Such a model comprises a number of parameters. While an analytical method determines model parameters, an evolutionary algorithm can improve them further. As evolutionary algorithms were designed on top of random-number generators, their results are not deterministic. This raises a concern about their applicability to medical devices. Medical-device algorithm must produce an output with a minimum guaranteed accuracy. Therefore, we applied de-randomized sequences to Meta-Differential Evolution instead of using a random-number generator. Eventually, we designed an optimization method based on zooming with derandomized sequences as an alternative to the Meta-Differential Evolution. As the experimental setup, we predicted glucose-level signal to cover a blind window of glucose-monitoring signal that results from a physiological lag in glucose transportation. Completely de-randomized differential evolution exhibited the same accuracy and precision as completely non-deterministic differential evolution. They produced 93% of glucose levels with relative error less than or equal to 15%.

Název v anglickém jazyce

De–randomized Meta-Differential Evolution for Calculating and Predicting Glucose Levels

Popis výsledku anglicky

A physiological model improves delivered healthcare, when constructing a medical device. Such a model comprises a number of parameters. While an analytical method determines model parameters, an evolutionary algorithm can improve them further. As evolutionary algorithms were designed on top of random-number generators, their results are not deterministic. This raises a concern about their applicability to medical devices. Medical-device algorithm must produce an output with a minimum guaranteed accuracy. Therefore, we applied de-randomized sequences to Meta-Differential Evolution instead of using a random-number generator. Eventually, we designed an optimization method based on zooming with derandomized sequences as an alternative to the Meta-Differential Evolution. As the experimental setup, we predicted glucose-level signal to cover a blind window of glucose-monitoring signal that results from a physiological lag in glucose transportation. Completely de-randomized differential evolution exhibited the same accuracy and precision as completely non-deterministic differential evolution. They produced 93% of glucose levels with relative error less than or equal to 15%.

Druh

D - Stať ve sborníku

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)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

2019 IEEE 32nd International Symposium on Computer-Based Medical Systems

ISBN

978-1-72812-286-1

ISSN

2372-9198

e-ISSN

—

Počet stran výsledku

6

Strana od-do

269-274

Název nakladatele

IEEE Computer Society Conference Publishing Services (CPS)

Místo vydání

Los Alamitos

Místo konání akce

Cordóba

Datum konání akce

5. 6. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000502356600055