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<https://discovery.ucl.ac.uk/id/eprint/10117791> <http://purl.org/dc/terms/title> "Personalized Dual-Hormone Control for Type 1 Diabetes Using Deep Reinforcement Learning"^^<http://www.w3.org/2001/XMLSchema#string> .
<https://discovery.ucl.ac.uk/id/eprint/10117791> <http://purl.org/ontology/bibo/abstract> "We introduce a dual-hormone control algorithm for people with Type 1 Diabetes (T1D) which uses deep reinforcement learning (RL). Specifically, double dilated recurrent neural networks are used to learn the control strategy, trained by a variant of Q-learning. The inputs to the model include the real-time sensed glucose and meal carbohydrate content, and the outputs are the actions necessary to deliver dual-hormone (basal insulin and glucagon) control. Without prior knowledge of the glucose-insulin metabolism, we develop a data-driven model using the UVA/Padova Simulator. We first pre-train a generalized model using long-term exploration in an environment with average T1D subject parameters provided by the simulator, then adopt importance sampling to train personalized models for each individual. In-silico, the proposed algorithm largely reduces adverse glycemic events, and achieves time in range, i.e., the percentage of normoglycemia,   93%  for the adults and   83%  for the adolescents, which outperforms previous approaches significantly. These results indicate that deep RL has great potential to improve the treatment of chronic diseases such as diabetes."^^<http://www.w3.org/2001/XMLSchema#string> .
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<https://discovery.ucl.ac.uk/id/document/1235559> <http://purl.org/dc/terms/hasPart> <https://discovery.ucl.ac.uk/id/eprint/10117791/6/indexcodes.txt> .
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<https://discovery.ucl.ac.uk/id/eprint/10117791/> <http://purl.org/dc/elements/1.1/title> "HTML Summary of #10117791 \n\nPersonalized Dual-Hormone Control for Type 1 Diabetes Using Deep Reinforcement Learning\n\n" .
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<https://discovery.ucl.ac.uk/id/eprint/10117791/> <http://xmlns.com/foaf/0.1/primaryTopic> <https://discovery.ucl.ac.uk/id/eprint/10117791> .