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 …
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 …
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 …
Reinforcement learning for intelligent healthcare applications: A survey
Discovering new treatments and personalizing existing ones is one of the major goals of
modern clinical research. In the last decade, Artificial Intelligence (AI) has enabled the …
modern clinical research. In the last decade, Artificial Intelligence (AI) has enabled the …
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 …
Enabling risk-aware Reinforcement Learning for medical interventions through uncertainty decomposition
Reinforcement Learning (RL) is emerging as tool for tackling complex control and decision-
making problems. However, in high-risk environments such as healthcare, manufacturing …
making problems. However, in high-risk environments such as healthcare, manufacturing …
Leveraging factored action spaces for efficient offline reinforcement learning in healthcare
Many reinforcement learning (RL) applications have combinatorial action spaces, where
each action is a composition of sub-actions. A standard RL approach ignores this inherent …
each action is a composition of sub-actions. A standard RL approach ignores this inherent …
Model selection for offline reinforcement learning: Practical considerations for healthcare settings
Reinforcement learning (RL) can be used to learn treatment policies and aid decision
making in healthcare. However, given the need for generalization over complex state/action …
making in healthcare. However, given the need for generalization over complex state/action …
Deep reinforcement learning for clinical decision support: a brief survey
Owe to the recent advancements in Artificial Intelligence especially deep learning, many
data-driven decision support systems have been implemented to facilitate medical doctors in …
data-driven decision support systems have been implemented to facilitate medical doctors in …
Evaluating reinforcement learning algorithms in observational health settings
Much attention has been devoted recently to the development of machine learning
algorithms with the goal of improving treatment policies in healthcare. Reinforcement …
algorithms with the goal of improving treatment policies in healthcare. Reinforcement …