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 …
Reinforcement learning for intelligent healthcare systems: A review of challenges, applications, and open research issues
The rise of chronic disease patients and the pandemic pose immediate threats to healthcare
expenditure and mortality rates. This calls for transforming healthcare systems away from …
expenditure and mortality rates. This calls for transforming healthcare systems away from …
Reinforcement learning for intelligent healthcare systems: A comprehensive survey
The rapid increase in the percentage of chronic disease patients along with the recent
pandemic pose immediate threats on healthcare expenditure and elevate causes of death …
pandemic pose immediate threats on healthcare expenditure and elevate causes of death …
Personalized multimorbidity management for patients with type 2 diabetes using reinforcement learning of electronic health records
Background Comorbid chronic conditions are common among people with type 2 diabetes.
We developed an artificial intelligence algorithm, based on reinforcement learning (RL), for …
We developed an artificial intelligence algorithm, based on reinforcement learning (RL), for …
Reinforcement learning-based expanded personalized diabetes treatment recommendation using South Korean electronic health records
Currently, electronic medical records are becoming more accessible to a growing number of
researchers seeking to develop personalized healthcare recommendations to aid …
researchers seeking to develop personalized healthcare recommendations to aid …
Challenges for reinforcement learning in healthcare
E Riachi, M Mamdani, M Fralick, F Rudzicz - arXiv preprint arXiv …, 2021 - arxiv.org
Many healthcare decisions involve navigating through a multitude of treatment options in a
sequential and iterative manner to find an optimal treatment pathway with the goal of an …
sequential and iterative manner to find an optimal treatment pathway with the goal of an …
Continuous-Time decision transformer for healthcare applications
Offline reinforcement learning (RL) is a promising approach for training intelligent medical
agents to learn treatment policies and assist decision making in many healthcare …
agents to learn treatment policies and assist decision making in many healthcare …
Reinforcement learning models and algorithms for diabetes management
With the advancements in reinforcement learning (RL), new variants of this artificial
intelligence approach have been introduced in the literature. This has led to increased …
intelligence approach have been introduced in the literature. This has led to increased …
Reinforcement learning in healthcare: A survey
As a subfield of machine learning, reinforcement learning (RL) aims at optimizing decision
making by using interaction samples of an agent with its environment and the potentially …
making by using interaction samples of an agent with its environment and the potentially …
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 …