Reinforcement Learning for Intelligent Healthcare Systems

Reinforcement Learning for Intelligent Healthcare Systems: A Review of Challenges, Applications, and Open Research Issues

Abdellatif, Alaa Awad (Qatar University)
Mhaisen, N. (TU Delft Networked Systems)
Mohamed, Amr (Qatar University)
Erbad, Aiman (Hamad Bin Khlifa University)
Guizani, Mohsen (Mohamed Bin Zayed University of Artificial Intelligence)

2023

The rise of chronic disease patients and the pandemic pose immediate threats to healthcare expenditure and mortality rates. This calls for transforming healthcare systems away from one-on-one patient treatment into intelligent health systems, leveraging the recent advances of Internet of Things and smart sensors. Meanwhile, reinforcement learning (RL) has witnessed an intrinsic breakthrough in solving a variety of complex problems for distinct applications and services. Thus, this article presents a comprehensive survey of the recent models and techniques of RL that have been developed/used for supporting Intelligent-healthcare (I-health) systems. It can guide the readers to deeply understand the state-of-the-art regarding the use of RL in the context of I-health. Specifically, we first present an overview of the I-health systems' challenges, architecture, and how RL can benefit these systems. We then review the background and mathematical modeling of different RL, deep RL (DRL), and multiagent RL models. We highlight important guidelines on how to select the appropriate RL model for a given problem, and provide quantitative comparisons, showing the results of deploying key RL models in two scenarios that can be followed in monitoring applications. After that, we conduct an in-depth literature review on RL's applications in I-health systems, covering edge intelligence, smart core network, and dynamic treatment regimes. Finally, we highlight emerging challenges and future research directions to enhance RL's success in I-health systems, which opens the door for exploring some interesting and unsolved problems.

Computer architecture
Deep learning
distributed machine learning
edge computing
Internet of Things
Internet of Things (IoT)
Mathematical models
Medical services
Optimization
Remote monitoring
remote monitoring
Surveys

http://resolver.tudelft.nl/uuid:fcb82a39-87a3-4478-80f8-adcbd21b69cb

https://doi.org/10.1109/JIOT.2023.3288050

2024-01-02

2327-4662

IEEE Internet of Things Journal, 10 (24), 21982-22007

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2023 Alaa Awad Abdellatif, N. Mhaisen, Amr Mohamed, Aiman Erbad, Mohsen Guizani