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 …
Electronic health records based reinforcement learning for treatment optimizing
Abstract Electronic Health Records (EHRs) have become one of the main sources of
evidence to evaluate clinical actions, improve medical quality, predict disease-risk, and …
evidence to evaluate clinical actions, improve medical quality, predict disease-risk, and …
Early prediction of diabetes mellitus using machine learning
G Tripathi, R Kumar - 2020 8th international conference on …, 2020 - ieeexplore.ieee.org
Diabetes mellitus is one of the noxious disease which causes abnormalities of blood
glucose due to the resistance of producing insulin hormone in the body. It affects various …
glucose due to the resistance of producing insulin hormone in the body. It affects various …
Forecasting the risk of type ii diabetes using reinforcement learning
Type II Diabetes (T2D) is one of the most common lifestyle diseases which is characterized
by insulin resistance. Lack of insulin's proper working causes uncontrollable blood glucose …
by insulin resistance. Lack of insulin's proper working causes uncontrollable blood glucose …
[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 …
Prediction of type-2 diabetes using classification and ensemble method approach
P Goyal, S Jain - 2022 International Mobile and Embedded …, 2022 - ieeexplore.ieee.org
Diabetes is a leading cause of blindness, renal failure, amputations, heart failure, and
stroke, among other complications. When we eat, our bodies convert the food we eat into …
stroke, among other complications. When we eat, our bodies convert the food we eat into …
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 …
[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 …
Control of type 1 diabetes mellitus using direct reinforcement learning based controller
One of the most challenging area of diabetes research is to provide such automated insulin
delivery systems–so called artificial pancreas systems–that have robust and adaptive …
delivery systems–so called artificial pancreas systems–that have robust and adaptive …
Evaluating Deep Q-Learning Techniques for Controlling Type 1 Diabetes
STN Hjerde - 2020 - munin.uit.no
Patients with type 1 diabetes (T1D) must continually decide how much insulin to inject
before each meal to maintain an acceptable level of blood glucose. Recent research has …
before each meal to maintain an acceptable level of blood glucose. Recent research has …