Reinforcement Learning Models and Algorithms for Diabetes Management

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.

Automated summary

This paper presents the application of various variants of reinforcement learning (RL) in diabetes management. It focuses on improving blood glucose levels and the similarity between RL and physician's policies. The paper discusses the attributes of RL, essential training elements, representation of diabetes attributes, and different types of RL algorithms. It also explores open issues and potential future developments in using RL for diabetes management.