Reducing Exercise-Related Hypoglycemia in Automated Insulin Delivery with Reinforcement Learning
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Author / Producer
Date
2024
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
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Data
Abstract
Exercise is an important component for glucose management in type 1 diabetes, but remains challenging for automated insulin delivery systems as altered glucose dynamics are difficult to model explicitly. Glucose monitoring data might enable data-driven approaches for learning these dynamics implicitly. We propose combining model predictive control with a reinforcement learning component to adjust basal insulin infusion rates for exercise. We train our model on a variety of exercise scenarios and demonstrate improved glucose control using a generically trained model. We further improve performance by personalizing the model with a small number of additional individual-specific training episodes.
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Publication status
published
External links
Editor
Book title
12th IFAC Symposium on Biological and Medical Systems (BMS 2024)
Journal / series
Volume
58 (24)
Pages / Article No.
239 - 244
Publisher
Elsevier
Event
12th IFAC Symposium on Biological and Medical Systems (BMS 2024)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Diabetes management; Decision support; Exercise; Hypoglycemia; Automated insulin delivery; Reinforcement learning
Organisational unit
03699 - Stelling, Jörg / Stelling, Jörg
Notes
Conference presentation on September 12, 2024