End-to-end offline reinforcement learning for glycemia control-Reference-Cited by-同舟云学术

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End-to-end offline reinforcement learning for glycemia control

Published:2024-08 Issue: Volume:154 Page:102920
ISSN:0933-3657
Container-title:Artificial Intelligence in Medicine
language:en
Short-container-title:Artificial Intelligence in Medicine

Author:

Beolet Tristan,Adenis Alice^ORCID,Huneker Erik,Louis Maxime

Publisher

Elsevier BV

Reference48 articles.

1. Diabeloop DBLG1 closed-loop system enables patients with type 1 diabetes to significantly improve their glycemic control in real-life situations without serious adverse events: 6-month follow-up;Amadou;Diabetes Care,2021

2. Minimed medtronic 780G optimizes glucose control in patients with type 1 diabetes mellitus;Pintaudi;Nutr Metab Cardiovasc Dis,2022

3. Review of the Omnipod® 5 automated glucose control system powered by HorizonTM for the treatment of type 1 diabetes;Cobry;Ther Deliv,2020

4. Lived experience of CamAPS FX closed loop system in youth with type 1 diabetes and their parents;Hood;Diabetes Obes Metab,2022

5. Safety and performance of the Tandem t: slim X2 with Control-IQ automated insulin delivery system in toddlers and preschoolers;Ekhlaspour;Diabetes Technol Ther,2021

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