A New Glycemic closed-loop control based on Dyna-Q for Type-1-Diabetes

Giorno, Silvia Del; D’Antoni, Federico; Piemonte, Vincenzo; Merone, Mario

doi:10.1016/j.bspc.2022.104492

Cited by 4 publications

(1 citation statement)

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“…That is in fact, the main problem of online RL. A potential alternative to alleviate this problem is to use a model-based RL approach, such as the recent one in [58], where a hypothesized insulin dose is simulated on a BG predictor before actually being injected to the patient. Its performance is good for simulations up to 12 hours and two meals but decreases in more realistic scenarios.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Offline Reinforcement Learning for Blood Glucose Level Control in Type 1 Diabetes

Viroonluecha,

Egea-Lopez,

Santa

2023

IEEE Access

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Patients with Type 1 diabetes must closely monitor their blood glucose levels and inject insulin to control them. Automated glucose control methods that remove the need for human intervention have been proposed, and recently reinforcement learning has been used as an effective control method in simulation environments. However, its real-world application would require trial and error interaction with patients. As an alternative, offline reinforcement learning does not require interaction with humans and initial studies suggest promising results can be obtained with offline datasets, similar to classical machine learning algorithms. However, its application to glucose control has not yet been evaluated. In this study, we evaluated two offline reinforcement learning algorithms for blood glucose control and discussed their potential and shortcomings. We also evaluated the influence on training and performance of the method that generates the training datasets, as well as the influence of the type of trajectories used (single-method or mixed trajectories), the quality of the trajectories, and the size of the datasets. Our results show that one of the offline reinforcement learning algorithms evaluated, Trajectory Transformer, is able to perform at the same level as commonly used baselines such as PID and Proximal Policy Optimization.INDEX TERMS T1D blood glucose control, Offline reinforcement learning, Transformer, Artificial pancreas, Machine learning As a solution, several methods for automated glucose

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Section: Related Workmentioning

confidence: 99%