A Review of model prediction in diabetes and of designing glucose regulators based on model predictive control for the artificial pancreas

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11130%2F17%3A10373961" target="_blank" >RIV/00216208:11130/17:10373961 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21230/17:00313140 RIV/68407700:21460/17:00313140 RIV/68407700:21730/17:00313140 RIV/00064203:_____/17:10373961

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-319-64265-9_6" target="_blank" >https://doi.org/10.1007/978-3-319-64265-9_6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-64265-9_6" target="_blank" >10.1007/978-3-319-64265-9_6</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Review of model prediction in diabetes and of designing glucose regulators based on model predictive control for the artificial pancreas

Popis výsledku v původním jazyce

The present work presents a comparative assessment of glucose prediction models for diabetic patients using data from sensors monitoring blood glucose concentration as well as data from in silico simulations. The models are based on neural networks and linear and nonlinear mathematical models evaluated for prediction horizons ranging from 5 to 120 min. Furthermore, the implementation of compartment models for simulation of absorption and elimination of insulin, caloric intake and information about physical activity is examined in combination with neural networks and mathematical models, respectively. This assessment also addresses the recent progress and challenges in designing glucose regulators based on model predictive control used as part of artificial pancreas devices for type 1 diabetic patients. The assessments include 24 papers in total, from 2006 to 2016, in order to investigate progress in blood glucose concentration prediction and in Artificial Pancreas devices for type 1 diabetic patients. (C) 2017, Springer International Publishing AG.

Název v anglickém jazyce

A Review of model prediction in diabetes and of designing glucose regulators based on model predictive control for the artificial pancreas

Popis výsledku anglicky

The present work presents a comparative assessment of glucose prediction models for diabetic patients using data from sensors monitoring blood glucose concentration as well as data from in silico simulations. The models are based on neural networks and linear and nonlinear mathematical models evaluated for prediction horizons ranging from 5 to 120 min. Furthermore, the implementation of compartment models for simulation of absorption and elimination of insulin, caloric intake and information about physical activity is examined in combination with neural networks and mathematical models, respectively. This assessment also addresses the recent progress and challenges in designing glucose regulators based on model predictive control used as part of artificial pancreas devices for type 1 diabetic patients. The assessments include 24 papers in total, from 2006 to 2016, in order to investigate progress in blood glucose concentration prediction and in Artificial Pancreas devices for type 1 diabetic patients. (C) 2017, Springer International Publishing AG.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

30202 - Endocrinology and metabolism (including diabetes, hormones)

Projekt

<a href="/cs/project/NV15-25710A" target="_blank" >NV15-25710A: Identifikace individuální dynamiky glykemických exkurzí u pacientů s diabetem pro zlepšení rozhodovacích postupů ovlivňujících dávkování inzulínu</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ISBN

978-3-319-64264-2

ISSN

0302-9743

e-ISSN

neuvedeno

Počet stran výsledku

16

Strana od-do

66-81

Název nakladatele

Springer Verlag

Místo vydání

Cham

Místo konání akce

Lyon

Datum konání akce

28. 8. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—