Name
Title | ||
|---|---|---|
Basal Glucose Control in Type 1 Diabetes Using Deep Reinforcement Learning: An <italic>In Silico</italic> Validation |
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/JBHI.2020.3014556 | IEEE Journal of Biomedical and Health Informatics |
Keywords | DocType | Volume |
Adolescent,Adult,Algorithms,Blood Glucose,Blood Glucose Self-Monitoring,Computer Simulation,Diabetes Mellitus, Type 1,Humans,Hypoglycemic Agents,Insulin,Insulin Infusion Systems,Pancreas, Artificial | Journal | 25 |
Issue | ISSN | Citations |
4 | 2168-2194 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Taiyu Zhu | 1 | 8 | 2.23 |
| Kezhi Li | 2 | 37 | 8.55 |
| Pau Herrero | 3 | 6 | 2.83 |
| Pantelis Georgiou | 4 | 111 | 50.64 |