The conjoining of dynamical systems and deep learning has become a topic of great
interest. In particular, neural differential equations (NDEs) demonstrate that neural networks …

Medical artificial intelligence (AI) systems have been remarkably successful, even
outperforming human performance at certain tasks. There is no doubt that AI is important to …

Offline reinforcement learning (RL) algorithms have shown promising results in domains
where abundant pre-collected data is available. However, prior methods focus on solving …

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 …

OBJECTIVE: Reinforcement learning (RL) is a machine learning technique uniquely
effective at sequential decision-making, which makes it potentially relevant to ICU treatment …

Abstract Machine learning has successfully framed many sequential decision making
problems as either supervised prediction, or optimal decision-making policy identification via …

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 …

Intensive care units (ICU) are increasingly looking towards machine learning for methods to
provide online monitoring of critically ill patients. In machine learning, online monitoring is …

Whenever a clinician reflects on the efficacy of a sequence of treatment decisions for a
patient, they may try to identify critical time steps where, had they made different decisions …

Sepsis is the leading cause of death in intensive care units. It is challenging to treat sepsis
because the optimal treatment is still unclear, and individual patients respond differently to …