Model-based reinforcement learning for type 1 diabetes blood glucose control

Yamagata, T.; O'Kane, A.; Ayobi, A.; Katz, D.; Stawarz, K.; Marshall, P.; Flach, P. and Santos-Rodriguez, R. (2020). Model-based reinforcement learning for type 1 diabetes blood glucose control. In: 1st International AAI4H - Advances in Artificial Intelligence for Healthcare Workshop, AAI4H 2020, 4 Sep 2020, [Virtual] Santiago de Compostela; Spain, pp. 72–77.



In this paper we investigate the use of model-based reinforcement learning to assist people with Type 1 Diabetes with insulin dose decisions. The proposed architecture consists of multiple Echo State Networks to predict blood glucose levels combined with Model Predictive Controller for planning. Echo State Network is a version of recurrent neural networks which allows us to learn long term dependencies in the input of time series data in an online manner. Additionally, we address the quantification of uncertainty for a more robust control. Here, we used ensembles of Echo State Networks to capture model (epistemic) uncertainty. We evaluated the approach with the FDA-approved UVa/Padova Type 1 Diabetes simulator and compared the results against baseline algorithms such as Basal-Bolus controller and Deep Q-learning. The results suggest that the modelbased reinforcement learning algorithm can perform equally or better than the baseline algorithms for the majority of virtual Type 1 Diabetes person profiles tested. Copyright © 2020 for this paper by its authors.

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