Cristin-resultat-ID: 1647890
Sist endret: 5. mars 2019 14:11
NVI-rapporteringsår: 2018
Resultat
Vitenskapelig artikkel
2018

Control of Blood Glucose for Type-1 Diabetes by Using Reinforcement Learning with Feedforward Algorithm

Bidragsytere:
  • Phuong Ngo
  • Susan Wei
  • Anna Holubova
  • Jan Muzik og
  • Fred Godtliebsen

Tidsskrift

Computational & Mathematical Methods in Medicine
ISSN 1748-670X
e-ISSN 1748-6718
NVI-nivå 1

Om resultatet

Vitenskapelig artikkel
Publiseringsår: 2018
Publisert online: 2018
Volum: 2018
Sider: 1 - 8
Open Access

Importkilder

Scopus-ID: 2-s2.0-85060164370

Finansiering

  • Tromsø forskningsstiftelse

    • Prosjektkode: A33027

Beskrivelse Beskrivelse

Tittel

Control of Blood Glucose for Type-1 Diabetes by Using Reinforcement Learning with Feedforward Algorithm

Sammendrag

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.

Bidragsytere

Phuong Ngo

  • Tilknyttet:
    Forfatter
    ved Institutt for matematikk og statistikk ved UiT Norges arktiske universitet

Susan Wei

  • Tilknyttet:
    Forfatter
    ved University of Melbourne

Anna Holubova

  • Tilknyttet:
    Forfatter
    ved Ceské vysoké ucení technické v Praze

Jan Muzik

  • Tilknyttet:
    Forfatter
    ved Ceské vysoké ucení technické v Praze

Fred Godtliebsen

  • Tilknyttet:
    Forfatter
    ved Institutt for matematikk og statistikk ved UiT Norges arktiske universitet
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