ub.xmlui.mirage2.page-structure.muninLogoub.xmlui.mirage2.page-structure.openResearchArchiveLogo
    • EnglishEnglish
    • norsknorsk
  • Velg spraakEnglish 
    • EnglishEnglish
    • norsknorsk
  • Administration/UB
View Item 
  •   Home
  • Fakultet for naturvitenskap og teknologi
  • Institutt for fysikk og teknologi
  • Artikler, rapporter og annet (fysikk og teknologi)
  • View Item
  •   Home
  • Fakultet for naturvitenskap og teknologi
  • Institutt for fysikk og teknologi
  • Artikler, rapporter og annet (fysikk og teknologi)
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

In-Silico Evaluation of Glucose Regulation Using Policy Gradient Reinforcement Learning for Patients with Type 1 Diabetes Mellitus

Permanent link
https://hdl.handle.net/10037/20656
DOI
https://doi.org/10.3390/app10186350
Thumbnail
View/Open
article.pdf (925.8Kb)
Published version (PDF)
Date
2020-09-11
Type
Journal article
Tidsskriftartikkel
Peer reviewed

Author
Myhre, Jonas Nordhaug; Tejedor Hernandez, Miguel Angel; Launonen, Ilkka Kalervo; El Fathi, Anas; Godtliebsen, Fred
Abstract
In this paper, we test and evaluate policy gradient reinforcement learning for automated blood glucose control in patients with Type 1 Diabetes Mellitus. Recent research has shown that reinforcement learning is a promising approach to accommodate the need for individualized blood glucose level control algorithms. The motivation for using policy gradient algorithms comes from the fact that adaptively administering insulin is an inherently continuous task. Policy gradient algorithms are known to be superior in continuous high-dimensional control tasks. Previously, most of the approaches for automated blood glucose control using reinforcement learning has used a finite set of actions. We use the Trust-Region Policy Optimization algorithm in this work. It represents the state of the art for deep policy gradient algorithms. The experiments are carried out in-silico using the Hovorka model, and stochastic behavior is modeled through simulated carbohydrate counting errors to illustrate the full potential of the framework. Furthermore, we use a model-free approach where no prior information about the patient is given to the algorithm. Our experiments show that the reinforcement learning agent is able to compete with and sometimes outperform state-of-the-art model predictive control in blood glucose regulation.
Is part of
Tejedor Hernández, M.Á. (2021). Glucose Regulation for In-Silico Type 1 Diabetes Patients Using Reinforcement Learning. (Doctoral thesis). https://hdl.handle.net/10037/20861.
Publisher
MDPI
Citation
Myhre, Tejedor Hernandez, Launonen, El Fathi, Godtliebsen. In-Silico Evaluation of Glucose Regulation Using Policy Gradient Reinforcement Learning for Patients with Type 1 Diabetes Mellitus. Applied Sciences. 2020
Metadata
Show full item record
Collections
  • Artikler, rapporter og annet (fysikk og teknologi) [1057]
Copyright 2020 The Author(s)

Browse

Browse all of MuninCommunities & CollectionsAuthor listTitlesBy Issue DateBrowse this CollectionAuthor listTitlesBy Issue Date
Login

Statistics

View Usage Statistics
UiT

Munin is powered by DSpace

UiT The Arctic University of Norway
The University Library
uit.no/ub - munin@ub.uit.no

Accessibility statement (Norwegian only)