A Review of model prediction in diabetes and of designing glucose regulators based on model predictive control for the artificial pancreas
The result's identifiers
Result code in 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>
Alternative codes found
RIV/68407700:21230/17:00313140 RIV/68407700:21460/17:00313140 RIV/68407700:21730/17:00313140 RIV/00064203:_____/17:10373961
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
A Review of model prediction in diabetes and of designing glucose regulators based on model predictive control for the artificial pancreas
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
30202 - Endocrinology and metabolism (including diabetes, hormones)
Result continuities
Project
<a href="/en/project/NV15-25710A" target="_blank" >NV15-25710A: Individual dynamics of glycaemia excursions identification in diabetic patients to improve self managing procedures influencing insulin dosage</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
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
Number of pages
16
Pages from-to
66-81
Publisher name
Springer Verlag
Place of publication
Cham
Event location
Lyon
Event date
Aug 28, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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