G2P2C — A modular reinforcement learning algorithm for glucose control by glucose prediction and planning in Type 1 Diabetes-Reference-Cited by-同舟云学术

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G2P2C — A modular reinforcement learning algorithm for glucose control by glucose prediction and planning in Type 1 Diabetes

Published:2024-04 Issue: Volume:90 Page:105839
ISSN:1746-8094
Container-title:Biomedical Signal Processing and Control
language:en
Short-container-title:Biomedical Signal Processing and Control

Author:

Hettiarachchi Chirath^ORCID,Malagutti Nicolo^ORCID,Nolan Christopher J.^ORCID,Suominen Hanna^ORCID,Daskalaki Elena^ORCID

Funder

Australian Government

National Cancer Institute

National Computational Infrastructure

Publisher

Elsevier BV

Subject

Health Informatics,Signal Processing,Biomedical Engineering

Reference94 articles.

1. Type 1 diabetes;DiMeglio;Lancet,2018

2. One year real-world use of the Control-IQ advanced hybrid closed-loop technology;Breton;Diabetes Technol. Therapeutics,2021

3. MiniMed 670G hybrid closed loop artificial pancreas system for the treatment of type 1 diabetes mellitus: Overview of its safety and efficacy;Saunders;Exp. Rev. Med. Dev.,2019

4. Hybrid closed-loop therapy: Where are we in 2021?;Leelarathna;Diabetes Obes. Metab.,2021

5. Algorithms for a closed-loop artificial pancreas: The case for proportional-integral-derivative control;Steil;J. Diabetes Sci. Technol.,2013

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. BGformer: An improved Informer model to enhance blood glucose prediction;Journal of Biomedical Informatics;2024-09

2. HypoTreat: Reducing Hypoglycemia in Artificial Pancreas Simulation;2024 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS);2024-05-23

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