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 …

As in other domains, artificial intelligence is becoming increasingly important in medicine. In
particular, deep learning-based pattern recognition methods can advance the field of …

Sepsis is the third leading cause of death worldwide and the main cause of mortality in
hospitals,–, but the best treatment strategy remains uncertain. In particular, evidence …

We consider a robust approach to address uncertainty in model parameters in Markov
decision processes (MDPs), which are widely used to model dynamic optimization in many …

OBJECTIVE: In the modern healthcare system, rapidly expanding costs/complexity, the
growing myriad of treatment options, and exploding information streams that often do not …

We provide a tutorial on the construction and evaluation of Markov decision processes
(MDPs), which are powerful analytical tools used for sequential decision making under …

This paper presents a review of the literature on multi-appointment scheduling problems in
hospitals. In these problems, patients need to sequentially visit multiple resource types in a …

Breast cancer is the most common nonskin cancer and the second leading cause of cancer
death in US women. Although mammography is the most effective modality for breast cancer …

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 …

How can risk analysts help to improve policy and decision making when the correct
probabilistic relation between alternative acts and their probable consequences is …