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 …

Machine learning for healthcare researchers face challenges to progress and reproducibility
due to a lack of standardized processing frameworks for public datasets. We present MIMIC …

The lack of interpretability remains a key barrier to the adoption of deep models in many
applications. In this work, we explicitly regularize deep models so human users might step …

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 …

Artificial intelligence in health care has experienced remarkable innovation and progress in
the last decade. Significant advancements can be attributed to the utilization of artificial …

Motivated by the many real-world applications of reinforcement learning (RL) that require
safe-policy iterations, we consider the problem of off-policy evaluation (OPE)---the problem …

Over the past few years, we have seen fundamental breakthroughs in core problems in
machine learning, largely driven by advances in deep neural networks. At the same time, the …

Deep reinforcement learning (DRL) has numerous applications in the real world, thanks to
its ability to achieve high performance in a range of environments with little manual …