[HTML][HTML] G2P2C—A modular reinforcement learning algorithm for glucose control by glucose prediction and planning in Type 1 Diabetes
Developing diagnostic and treatment solutions for medical applications is often challenging
due to the complex dynamics, partial observability, high inter-and intra-population variability …
due to the complex dynamics, partial observability, high inter-and intra-population variability …
Adaptive personalized prior-knowledge-informed model predictive control for type 1 diabetes
This work considers the problem of adaptive prior-informed model predictive control (MPC)
formulations that explicitly incorporate prior knowledge in the model development and is …
formulations that explicitly incorporate prior knowledge in the model development and is …
Basal-bolus advisor for type 1 diabetes (T1D) patients using multi-agent reinforcement learning (RL) methodology
This study presents in-silico design and verification of an advanced multi-agent
reinforcement learning (RL) strategy for personalized glucose regulation in individuals …
reinforcement learning (RL) strategy for personalized glucose regulation in individuals …
A reinforcement learning based system for blood glucose control without carbohydrate estimation in type 1 diabetes: In silico validation
Type 1 Diabetes (T1D) is a chronic autoimmune disease, which requires the use of
exogenous insulin for glucose regulation. In current hybrid closed-loop systems, meal entry …
exogenous insulin for glucose regulation. In current hybrid closed-loop systems, meal entry …
ARLPE: A meta reinforcement learning framework for glucose regulation in type 1 diabetics
External artificial pancreas with autonomous control algorithms has proved its effectiveness
in glucose regulation for type 1 diabetes. Nonetheless, most existing algorithms cannot …
in glucose regulation for type 1 diabetes. Nonetheless, most existing algorithms cannot …
Data-enabled learning and control algorithms for intelligent glucose management: the state of the art
External insulin administration is an effective way for patients with diabetes mellitus to
regulate their blood glucose. Multiple daily injections (MDIs), sensor-augmented pump …
regulate their blood glucose. Multiple daily injections (MDIs), sensor-augmented pump …
[HTML][HTML] Evaluating Deep Q-Learning Algorithms for Controlling Blood Glucose in In Silico Type 1 Diabetes
M Tejedor, SN Hjerde, JN Myhre, F Godtliebsen - Diagnostics, 2023 - mdpi.com
Patients with type 1 diabetes must continually decide how much insulin to inject before each
meal to maintain blood glucose levels within a healthy range. Recent research has worked …
meal to maintain blood glucose levels within a healthy range. Recent research has worked …
[HTML][HTML] In-silico evaluation of glucose regulation using policy gradient reinforcement learning for patients with type 1 diabetes mellitus
J Nordhaug Myhre, M Tejedor, I Kalervo Launonen… - Applied Sciences, 2020 - mdpi.com
In this paper, we test and evaluate policy gradient reinforcement learning for automated
blood glucose control in patients with Type 1 Diabetes Mellitus. Recent research has shown …
blood glucose control in patients with Type 1 Diabetes Mellitus. Recent research has shown …
[HTML][HTML] Generation of post-meal insulin correction boluses in type 1 diabetes simulation models for in-silico clinical trials: More realistic scenarios obtained using a …
Background and objective In type 1 diabetes (T1D) research, in-silico clinical trials (ISCTs)
notably facilitate the design/testing of new therapies. Published simulation tools embed …
notably facilitate the design/testing of new therapies. Published simulation tools embed …
Hybrid control policy for artificial pancreas via ensemble deep reinforcement learning
Objective: The artificial pancreas (AP) has shown promising potential in achieving closed-
loop glucose control for individuals with type 1 diabetes mellitus (T1DM). However …
loop glucose control for individuals with type 1 diabetes mellitus (T1DM). However …