-
Something wrong with this record ?
Control of Blood Glucose for Type-1 Diabetes by Using Reinforcement Learning with Feedforward Algorithm
PD. Ngo, S. Wei, A. Holubová, J. Muzik, F. Godtliebsen,
Language English Country United States
Document type Evaluation Study, Journal Article
NLK
Free Medical Journals
from 2011
PubMed Central
from 2011
Europe PubMed Central
from 2011
Open Access Digital Library
from 1997-01-01
Open Access Digital Library
from 2006-01-01
Open Access Digital Library
from 2011-01-01
Medline Complete (EBSCOhost)
from 2006-03-01 to 2023-06-29
Wiley-Blackwell Open Access Titles
from 1997
PubMed
30693047
DOI
10.1155/2018/4091497
Knihovny.cz E-resources
- MeSH
- Algorithms * MeSH
- Models, Biological MeSH
- Diabetes Mellitus, Type 1 blood drug therapy MeSH
- Insulin administration & dosage MeSH
- Kinetics MeSH
- Blood Glucose metabolism MeSH
- Humans MeSH
- Therapy, Computer-Assisted statistics & numerical data MeSH
- Computer Simulation MeSH
- Reinforcement, Psychology MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Evaluation Study MeSH
Background: Type-1 diabetes is a condition caused by the lack of insulin hormone, which leads to an excessive increase in blood glucose level. The glucose kinetics process is difficult to control due to its complex and nonlinear nature and with state variables that are difficult to measure. Methods: This paper proposes a method for automatically calculating the basal and bolus insulin doses for patients with type-1 diabetes using reinforcement learning with feedforward controller. The algorithm is designed to keep the blood glucose stable and directly compensate for the external events such as food intake. Its performance was assessed using simulation on a blood glucose model. The usage of the Kalman filter with the controller was demonstrated to estimate unmeasurable state variables. Results: Comparison simulations between the proposed controller with the optimal reinforcement learning and the proportional-integral-derivative controller show that the proposed methodology has the best performance in regulating the fluctuation of the blood glucose. The proposed controller also improved the blood glucose responses and prevented hypoglycemia condition. Simulation of the control system in different uncertain conditions provided insights on how the inaccuracies of carbohydrate counting and meal-time reporting affect the performance of the control system. Conclusion: The proposed controller is an effective tool for reducing postmeal blood glucose rise and for countering the effects of external known events such as meal intake and maintaining blood glucose at a healthy level under uncertainties.
References provided by Crossref.org
- 000
- 00000naa a2200000 a 4500
- 001
- bmc19011946
- 003
- CZ-PrNML
- 005
- 20190411101001.0
- 007
- ta
- 008
- 190405s2018 xxu f 000 0|eng||
- 009
- AR
- 024 7_
- $a 10.1155/2018/4091497 $2 doi
- 035 __
- $a (PubMed)30693047
- 040 __
- $a ABA008 $b cze $d ABA008 $e AACR2
- 041 0_
- $a eng
- 044 __
- $a xxu
- 100 1_
- $a Ngo, Phuong D $u UiT The Arctic University of Norway, Tromsø, Norway.
- 245 10
- $a Control of Blood Glucose for Type-1 Diabetes by Using Reinforcement Learning with Feedforward Algorithm / $c PD. Ngo, S. Wei, A. Holubová, J. Muzik, F. Godtliebsen,
- 520 9_
- $a Background: Type-1 diabetes is a condition caused by the lack of insulin hormone, which leads to an excessive increase in blood glucose level. The glucose kinetics process is difficult to control due to its complex and nonlinear nature and with state variables that are difficult to measure. Methods: This paper proposes a method for automatically calculating the basal and bolus insulin doses for patients with type-1 diabetes using reinforcement learning with feedforward controller. The algorithm is designed to keep the blood glucose stable and directly compensate for the external events such as food intake. Its performance was assessed using simulation on a blood glucose model. The usage of the Kalman filter with the controller was demonstrated to estimate unmeasurable state variables. Results: Comparison simulations between the proposed controller with the optimal reinforcement learning and the proportional-integral-derivative controller show that the proposed methodology has the best performance in regulating the fluctuation of the blood glucose. The proposed controller also improved the blood glucose responses and prevented hypoglycemia condition. Simulation of the control system in different uncertain conditions provided insights on how the inaccuracies of carbohydrate counting and meal-time reporting affect the performance of the control system. Conclusion: The proposed controller is an effective tool for reducing postmeal blood glucose rise and for countering the effects of external known events such as meal intake and maintaining blood glucose at a healthy level under uncertainties.
- 650 12
- $a algoritmy $7 D000465
- 650 _2
- $a krevní glukóza $x metabolismus $7 D001786
- 650 _2
- $a počítačová simulace $7 D003198
- 650 _2
- $a diabetes mellitus 1. typu $x krev $x farmakoterapie $7 D003922
- 650 _2
- $a lidé $7 D006801
- 650 _2
- $a inzulin $x aplikace a dávkování $7 D007328
- 650 _2
- $a kinetika $7 D007700
- 650 _2
- $a biologické modely $7 D008954
- 650 _2
- $a posilování (psychologie) $7 D012054
- 650 _2
- $a počítačem asistovaná terapie $x statistika a číselné údaje $7 D013813
- 655 _2
- $a hodnotící studie $7 D023362
- 655 _2
- $a časopisecké články $7 D016428
- 700 1_
- $a Wei, Susan $u The University of Melbourne, Australia.
- 700 1_
- $a Holubová, Anna $u Czech Technical University, Prague, Czech Republic.
- 700 1_
- $a Muzik, Jan $u Czech Technical University, Prague, Czech Republic.
- 700 1_
- $a Godtliebsen, Fred $u UiT The Arctic University of Norway, Tromsø, Norway.
- 773 0_
- $w MED00173439 $t Computational and mathematical methods in medicine $x 1748-6718 $g Roč. 2018, č. - (2018), s. 4091497
- 856 41
- $u https://pubmed.ncbi.nlm.nih.gov/30693047 $y Pubmed
- 910 __
- $a ABA008 $b sig $c sign $y a $z 0
- 990 __
- $a 20190405 $b ABA008
- 991 __
- $a 20190411101018 $b ABA008
- 999 __
- $a ok $b bmc $g 1391256 $s 1050251
- BAS __
- $a 3
- BAS __
- $a PreBMC
- BMC __
- $a 2018 $b 2018 $c - $d 4091497 $e 20181230 $i 1748-6718 $m Computational and mathematical methods in medicine $n Comput Math Methods Med $x MED00173439
- LZP __
- $a Pubmed-20190405