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 …

Background Decision support systems based on reinforcement learning (RL) have been
implemented to facilitate the delivery of personalized care. This paper aimed to provide a …

Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has
become one of the most important and useful technology. It is a learning method where a …

Dynamic treatment recommendation systems based on large-scale electronic health records
(EHRs) become a key to successfully improve practical clinical outcomes. Prior relevant …

Background Reinforcement learning (RL) is a computational approach to understanding and
automating goal-directed learning and decision-making. It is designed for problems which …

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 …

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 …

People with type 1 diabetes (T1D) lack the ability to produce the insulin their bodies need.
As a result, they must continually make decisions about how much insulin to self-administer …

Single-cell technologies are revolutionizing the entire field of biology. The large volumes of
data generated by single-cell technologies are high dimensional, sparse, and …

Background Reinforcement learning (RL) provides a promising technique to solve complex
sequential decision making problems in health care domains. To ensure such applications …