  As a subfield of machine learning, reinforcement learning (RL) aims at
empowering one's capabilities in behavioural decision making by using
interaction experience with the world and an evaluative feedback. Unlike
traditional supervised learning methods that usually rely on one-shot,
exhaustive and supervised reward signals, RL tackles with sequential decision
making problems with sampled, evaluative and delayed feedback simultaneously.
Such distinctive features make RL technique a suitable candidate for developing
powerful solutions in a variety of healthcare domains, where diagnosing
decisions or treatment regimes are usually characterized by a prolonged and
sequential procedure. This survey discusses the broad applications of RL
techniques in healthcare domains, in order to provide the research community
with systematic understanding of theoretical foundations, enabling methods and
techniques, existing challenges, and new insights of this emerging paradigm. By
first briefly examining theoretical foundations and key techniques in RL
research from efficient and representational directions, we then provide an
overview of RL applications in healthcare domains ranging from dynamic
treatment regimes in chronic diseases and critical care, automated medical
diagnosis from both unstructured and structured clinical data, as well as many
other control or scheduling domains that have infiltrated many aspects of a
healthcare system. Finally, we summarize the challenges and open issues in
current research, and point out some potential solutions and directions for
future research.
