[HTML][HTML] Offline reinforcement learning for safer blood glucose control in people with type 1 diabetes
The widespread adoption of effective hybrid closed loop systems would represent an
important milestone of care for people living with type 1 diabetes (T1D). These devices …
important milestone of care for people living with type 1 diabetes (T1D). These devices …
Deep reinforcement learning for closed-loop blood glucose control
I Fox, J Lee, R Pop-Busui… - Machine Learning for …, 2020 - proceedings.mlr.press
People with type 1 diabetes (T1D) lack the ability to produce the insulin their bodies need.
As a result, they must continually make decisions about how much insulin to self-administer …
As a result, they must continually make decisions about how much insulin to self-administer …
Reinforcement learning for blood glucose control: Challenges and opportunities
Individuals with type 1 diabetes (T1D) lack the ability to produce the insulin their bodies
need. As a result, they must continually make decisions about how much insulin to self …
need. As a result, they must continually make decisions about how much insulin to self …
Offline deep reinforcement learning and off-policy evaluation for personalized basal insulin control in type 1 diabetes
Recent advancements in hybrid closed-loop systems, also known as the artificial pancreas
(AP), have been shown to optimize glucose control and reduce the self-management …
(AP), have been shown to optimize glucose control and reduce the self-management …
A blood glucose control framework based on reinforcement learning with safety and interpretability: In silico validation
Controlling blood glucose levels in diabetic patients is important for managing their health
and quality of life. Several algorithms based on model predictive control and reinforcement …
and quality of life. Several algorithms based on model predictive control and reinforcement …
Reinforcement learning application in diabetes blood glucose control: A systematic review
M Tejedor, AZ Woldaregay, F Godtliebsen - Artificial intelligence in …, 2020 - Elsevier
Background Reinforcement learning (RL) is a computational approach to understanding and
automating goal-directed learning and decision-making. It is designed for problems which …
automating goal-directed learning and decision-making. It is designed for problems which …
Evaluation of blood glucose level control in type 1 diabetic patients using deep reinforcement learning
Diabetes mellitus is a disease associated with abnormally high levels of blood glucose due
to a lack of insulin. Combining an insulin pump and continuous glucose monitor with a …
to a lack of insulin. Combining an insulin pump and continuous glucose monitor with a …
Optimized glycemic control of type 2 diabetes with reinforcement learning: a proof-of-concept trial
G Wang, X Liu, Z Ying, G Yang, Z Chen, Z Liu… - Nature Medicine, 2023 - nature.com
The personalized titration and optimization of insulin regimens for treatment of type 2
diabetes (T2D) are resource-demanding healthcare tasks. Here we propose a model-based …
diabetes (T2D) are resource-demanding healthcare tasks. Here we propose a model-based …
An insulin bolus advisor for type 1 diabetes using deep reinforcement learning
(1) Background: People living with type 1 diabetes (T1D) require self-management to
maintain blood glucose (BG) levels in a therapeutic range through the delivery of exogenous …
maintain blood glucose (BG) levels in a therapeutic range through the delivery of exogenous …
Toward a fully automated artificial pancreas system using a bioinspired reinforcement learning design: In silico validation
Objective: The automation of insulin treatment is the most challenge aspect of glucose
management for type 1 diabetes owing to unexpected exogenous events (eg, meal intake) …
management for type 1 diabetes owing to unexpected exogenous events (eg, meal intake) …