Bias in reinforcement learning: A review in healthcare applications
Reinforcement learning (RL) can assist in medical decision making using patient data
collected in electronic health record (EHR) systems. RL, a type of machine learning, can use …
collected in electronic health record (EHR) systems. RL, a type of machine learning, can use …
Does reinforcement learning improve outcomes for critically ill patients? A systematic review and level-of-readiness assessment
M Otten, AR Jagesar, TA Dam, LA Biesheuvel… - Critical Care …, 2024 - journals.lww.com
OBJECTIVE: Reinforcement learning (RL) is a machine learning technique uniquely
effective at sequential decision-making, which makes it potentially relevant to ICU treatment …
effective at sequential decision-making, which makes it potentially relevant to ICU treatment …
[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 …
FedCSCD-GAN: A secure and collaborative framework for clinical cancer diagnosis via optimized federated learning and GAN
Digital technologies present unrivaled opportunities to improve healthcare services
worldwide. Medical devices and hospitals are now using innovative techniques to diagnose …
worldwide. Medical devices and hospitals are now using innovative techniques to diagnose …
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 …
Deep reinforcement learning-based control of chemo-drug dose in cancer treatment
H Mashayekhi, M Nazari, F Jafarinejad… - Computer Methods and …, 2024 - Elsevier
Background and objective Advancement in the treatment of cancer, as a leading cause of
death worldwide, has promoted several research activities in various related fields. The …
death worldwide, has promoted several research activities in various related fields. The …
AI-driven patient monitoring with multi-agent deep reinforcement learning
Effective patient monitoring is vital for timely interventions and improved healthcare
outcomes. Traditional monitoring systems often struggle to handle complex, dynamic …
outcomes. Traditional monitoring systems often struggle to handle complex, dynamic …
[Retracted] Design and Application of Artificial Intelligence Technology‐Driven Education and Teaching System in Universities
F Zhang - Computational and Mathematical Methods in …, 2022 - Wiley Online Library
In recent years, many colleges and universities have been experimenting and exploring the
evaluation of education and teaching system and have achieved certain results. In order to …
evaluation of education and teaching system and have achieved certain results. In order to …
Randomization tests for adaptively collected data
Y Nair, L Janson - arXiv preprint arXiv:2301.05365, 2023 - arxiv.org
Randomization testing is a fundamental method in statistics, enabling inferential tasks such
as testing for (conditional) independence of random variables, constructing confidence …
as testing for (conditional) independence of random variables, constructing confidence …
MSLR: A Self-supervised Representation Learning Method for Tabular Data Based on Multi-scale Ladder Reconstruction
Tabular data are widely used for prediction tasks, but they often suffer from the curse of
dimensionality and noise, leading to degradation in the performance and robustness of …
dimensionality and noise, leading to degradation in the performance and robustness of …