Reinforcement Learning in Healthcare: A Survey

Chao Yu, Jiming Liu, Shamim Nemati, Guosheng Yin

Research output: Contribution to journalJournal articlepeer-review

193 Citations (Scopus)

Abstract

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 delayed feedbacks. In contrast to traditional supervised learning that typically relies on one-shot, exhaustive, and supervised reward signals, RL tackles sequential decision-making problems with sampled, evaluative, and delayed feedbacks simultaneously. Such a distinctive feature makes RL techniques a suitable candidate for developing powerful solutions in various healthcare domains, where diagnosing decisions or treatment regimes are usually characterized by a prolonged period with delayed feedbacks. By first briefly examining theoretical foundations and key methods in RL research, this survey provides an extensive overview of RL applications in a variety of healthcare domains, ranging from dynamic treatment regimes in chronic diseases and critical care, automated medical diagnosis, and many other control or scheduling problems that have infiltrated every aspect of the healthcare system. In addition, we discuss the challenges and open issues in the current research and highlight some potential solutions and directions for future research.

Original languageEnglish
Article number5
Pages (from-to)1–36
Number of pages36
JournalACM Computing Surveys
Volume55
Issue number1
Early online date23 Nov 2021
DOIs
Publication statusPublished - Jan 2023

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • automated diagnosis
  • chronic disease
  • critical care
  • dynamic treatment regimes
  • healthcare
  • Reinforcement learning

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