A Reinforcement Learning Based System for Blood Glucose Control without Carbohydrate Estimation in Type 1 Diabetes: In Silico Validation-Reference-Cited by-同舟云学术

A Reinforcement Learning Based System for Blood Glucose Control without Carbohydrate Estimation in Type 1 Diabetes: In Silico Validation

Published:2022-07-11 Issue: Volume: Page:
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Container-title:2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
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Author:

Hettiarachchi Chirath¹,Malagutti Nicolo²,Nolan Christopher³,Daskalaki Eleni¹,Suominen Hanna¹

Affiliation:

1. School of Computing, College of Engineering & Computer Science (CECS), Australian National University (ANU),Canberra ACT,Australia

2. School of Engineering, CECS, ANU,Canberra ACT,Australia

3. ANU Medical School, College of Health and Medicine, ANU,Canberra ACT,Australia

Funder

Australian National University

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9870821/9870822/09871054.pdf?arnumber=9871054

Reference56 articles.

1. CONTROLLING BLOOD GLUCOSE LEVELS IN PATIENTS WITH TYPE 1 DIABETES USING FITTED Q-ITERATIONS AND FUNCTIONAL FEATURES

2. Reinforcement learning for blood glucose control: Challenges and opportunities;fox;Reinforcement Learning for Real Life (RL4RealLife) Workshop in the 36th International Conference on Machine Learning,0

3. A Dual Mode Adaptive Basal-Bolus Advisor Based on Reinforcement Learning

4. Personalized tuning of a reinforcement learning control algorithm for glucose regulation

5. An Actor–Critic based controller for glucose regulation in type 1 diabetes

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