System Development & Application
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 9
2765-2769

Study on insulin administration strategy of type 1 diabetes based on reinforcement learning

Jiao Zehui1
Xie Baisen1
Sun Fuquan2
1. College of Information Science & Engineering, North Eastern University, Shenyang 110000, China
2. College of Mathematics & Statistics, North Eastern University at Qinhuangdao, Qinhuangdao Hebei 066000, China
DOI: 10.19734/j.issn.1001-3695.2023.02.0052

Abstract

Type 1 diabetes(T1D) patients need to maintain blood glucose(BG) within the treatment range through the delivery of exogenous insulin. At present, several existing insulin administration algorithms based on model predictive control and reinforcement learning(RL) have problems such as poor sample efficiency, overly simple reward mechanisms, and poor blood glucose regulation effects. This paper proposed an IASGN strategy based on reinforcement learning. Aiming at the characteristics of safety and rapidity of the administration strategy, it introduced cumulative plot rewards and classified experience playback methods, increased elite sample pool according to different importance sampling weights, trained the administration guidance network based on the elite sample pool to guide the action of the strategy network, and improved the reward mechanism. It verified the performance of the proposed method in the FDA approved UVA/Padova T1D simulator. The results show that the TIR of the proposed method reaches 98.21%, and the TBR is close to 0. All patients in CVGA are within the safe range of A+B zone, which can keep their blood sugar within the normal range for a long time and avoid the risk of hypoglycemia. Compared with the benchmark methods, it also achieved better performance.

Foundation Support

国家重点研发计划资助项目(2018YFB1402800)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.02.0052
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 9
Section: System Development & Application
Pages: 2765-2769
Serial Number: 1001-3695(2023)09-031-2765-05

Publish History

[2023-05-12] Accepted Paper
[2023-09-05] Printed Article

Cite This Article

焦泽辉, 解柏森, 孙福权. 基于强化学习的1型糖尿病胰岛素给药策略研究 [J]. 计算机应用研究, 2023, 40 (9): 2765-2769. (Jiao Zehui, Xie Baisen, Sun Fuquan. Study on insulin administration strategy of type 1 diabetes based on reinforcement learning [J]. Application Research of Computers, 2023, 40 (9): 2765-2769. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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