Reinforcement Learning Models and Algorithms for Diabetes Management

Kok-Lim Alvin Yau, Yung-Wey Chong, Xiumei Fan, Celimuge Wu, Yasir Saleem, Phei-Ching Lim

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygl adolyguadolygiad gan gymheiriaid

41 Wedi eu Llwytho i Lawr (Pure)


With the advancements in reinforcement learning (RL), new variants of this artificial intelligence approach have been introduced in the literature. This has led to increased interest in using RL to address complex issues in diabetes management. Using RL, a decision maker (or agent) observes decision-making factors (or state) from the dynamic operating environment, selects actions, and subsequently receives delayed rewards. The agent adapts its actions to changes in the operating environment to maximize its cumulative reward and improve system performance. This paper presents how various variants of RL have been used to improve diabetes management, such as a higher time in range during which the blood glucose level is within the normal range and a higher similarity between RL and physician's policies. Key highlights focus on the application of RL in diabetes management, including a taxonomy of the attributes of RL (e.g., roles and advantages), essential elements for training (e.g., data and simulators), representations of diabetes attributes in RL models, and variants of RL algorithms. In addition, this paper discusses open issues and potential future developments in the use of RL in diabetes management.

Iaith wreiddiolSaesneg
Tudalennau (o-i)28391-28415
Nifer y tudalennau25
CyfnodolynIEEE Access
Dyddiad ar-lein cynnar20 Maw 2023
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 24 Maw 2023

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