Zhu, T;
Li, K;
Herrero, P;
Georgiou, P;
(2021)
Basal Glucose Control in Type 1 Diabetes Using Deep Reinforcement Learning: An >In Silico Validation.
IEEE Journal of Biomedical and Health Informatics
, 25
(4)
pp. 1223-1232.
10.1109/jbhi.2020.3014556.
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Abstract
People with Type 1 diabetes (T1D) require regular exogenous infusion of insulin to maintain their blood glucose concentration in a therapeutically adequate target range. Although the artificial pancreas and continuous glucose monitoring have been proven to be effective in achieving closed-loop control, significant challenges still remain due to the high complexity of glucose dynamics and limitations in the technology. In this work, we propose a novel deep reinforcement learning model for single-hormone (insulin) and dual-hormone (insulin and glucagon) delivery. In particular, the delivery strategies are developed by double Q-learning with dilated recurrent neural networks. For designing and testing purposes, the FDA-accepted UVA/Padova Type 1 simulator was employed. First, we performed long-term generalized training to obtain a population model. Then, this model was personalized with a small data-set of subject-specific data. In silico results show that the single and dual-hormone delivery strategies achieve good glucose control when compared to a standard basal-bolus therapy with low-glucose insulin suspension. Specifically, in the adult cohort (n = 10), percentage time in target range 70, 180 mg/dL improved from 77.6% to 80.9% with single-hormone control, and to 85.6% with dual-hormone control. In the adolescent cohort (n = 10), percentage time in target range improved from 55.5% to 65.9% with single-hormone control, and to 78.8% with dual-hormone control. In all scenarios, a significant decrease in hypoglycemia was observed. These results show that the use of deep reinforcement learning is a viable approach for closed-loop glucose control in T1D.
| Type: | Article |
|---|---|
| Title: | Basal Glucose Control in Type 1 Diabetes Using Deep Reinforcement Learning: An >In Silico Validation |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/jbhi.2020.3014556 |
| Publisher version: | http://dx.doi.org/10.1109/jbhi.2020.3014556 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Insulin, Sugar, Diabetes, Neural networks, Machine learning, Blood, Pancreas |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10126485 |
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