eprintid: 10117791
rev_number: 17
eprint_status: archive
userid: 608
dir: disk0/10/11/77/91
datestamp: 2020-12-18 14:26:35
lastmod: 2021-12-10 23:38:56
status_changed: 2020-12-18 14:26:35
type: proceedings_section
metadata_visibility: show
creators_name: Zhu, T
creators_name: Li, K
creators_name: Georgiou, P
title: Personalized Dual-Hormone Control for Type 1 Diabetes Using Deep Reinforcement Learning
ispublished: pub
divisions: UCL
divisions: B02
divisions: DD4
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
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.
date: 2020
date_type: published
publisher: Springer
official_url: https://doi.org/10.1007/978-3-030-53352-6_5
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1836425
doi: 10.1007/978-3-030-53352-6_5
isbn_13: 9783030533519
lyricists_name: Li, Kezhi
lyricists_id: KLIXX57
actors_name: Li, Kezhi
actors_id: KLIXX57
actors_role: owner
full_text_status: public
series: Studies in Computational Intelligence
publication: Studies in Computational Intelligence
volume: 914
place_of_pub: Cham, Switzerland
pagerange: 45-53
book_title: Explainable AI in Healthcare and Medicine
editors_name: Shaban-Nejad, A
editors_name: Michalowski, M
editors_name: Buckeridge, DL
citation:        Zhu, T;    Li, K;    Georgiou, P;      (2020)    Personalized Dual-Hormone Control for Type 1 Diabetes Using Deep Reinforcement Learning.                     In: Shaban-Nejad, A and Michalowski, M and Buckeridge, DL, (eds.) Explainable AI in Healthcare and Medicine.  (pp. pp. 45-53).  Springer: Cham, Switzerland.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10117791/1/Personalized%20Dual-Hormone%20Control%20for%20Type%201%20Diabetes%20Using%20DeepReinforcement%20Learning.pdf