Reinforcement Learning in Healthcare: A Survey

By Chao Yu, Jiming Liu, Shamim Nemati. 2019

788 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Cardiology and Cardiovascular Medicine, Surgery, Reinforcement Learning

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In Artificial Intelligence

In Cardiology and Cardiovascular Medicine

Cynthia Rudin

In Surgery

Cynthia Rudin

In Reinforcement Learning

Citations in the corpus

The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models (2022, background): “These examples show that better algorithms and models are not enough; for human-centered applications such as healthcare (Yu et al., 2019), economics (Trott et al., 2021) and robotics (Kober et al., 2013), RL algorithms must be safe and aligned with human objectives (Bommasani et al., 2021;…”
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, background): “medicine, including approaches for dynamic treatment regimes [190, 326], treatment of sepsis [162], treatment of chronic disorders including epilepsy [117], for HIV therapy [230], and management of Type 1 diabetes [136].”
Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes (2024, background): “…their combination with deep neural networks referred to as deep RL (DRL) (2), have shown remarkable capabilities in solving complex decision-making problems even with high-dimensional observations in domains such as board games (3), video games (4), healthcare (5), and recommendation systems (6).”
Remote patient monitoring using artificial intelligence: Current state, applications, and challenges (2023, methodology): “It can employ trial and error to solve a problem and get either rewards or penalties for the steps it executes (C. Yu et al., 2023).”