Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Survey

• URL: http://arxiv.org/abs/2108.04087v1
• Date: Thu, 5 Aug 2021 18:47:17 GMT
• Title: Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Survey
• Authors: Alaa Awad Abdellatif, Naram Mhaisen, Zina Chkirbene, Amr Mohamed, Aiman Erbad, Mohsen Guizani
• Abstract summary: Reinforcement Learning (RL) has witnessed an intrinsic breakthrough in solving a variety of complex problems for diverse applications and services. This paper can guide the readers to deeply understand the state-of-the-art regarding the use of RL in the context of I-health.
• Score: 42.17523380108375
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The rapid increase in the percentage of chronic disease patients along with the recent pandemic pose immediate threats on healthcare expenditure and elevate causes of death. This calls for transforming healthcare systems away from one-on-one patient treatment into intelligent health systems, to improve services, access and scalability, while reducing costs. Reinforcement Learning (RL) has witnessed an intrinsic breakthrough in solving a variety of complex problems for diverse applications and services. Thus, we conduct in this paper a comprehensive survey of the recent models and techniques of RL that have been developed/used for supporting Intelligent-healthcare (I-health) systems. This paper 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 for 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 multi-agent RL models. After that, we provide a deep literature review for the applications of RL in I-health systems. In particular, three main areas have been tackled, i.e., edge intelligence, smart core network, and dynamic treatment regimes. Finally, we highlight emerging challenges and outline future research directions in driving the future success of RL in I-health systems, which opens the door for exploring some interesting and unsolved problems.

Related papers

• A Survey of Artificial Intelligence in Gait-Based Neurodegenerative Disease Diagnosis [51.07114445705692]
  neurodegenerative diseases (NDs) traditionally require extensive healthcare resources and human effort for medical diagnosis and monitoring. As a crucial disease-related motor symptom, human gait can be exploited to characterize different NDs. The current advances in artificial intelligence (AI) models enable automatic gait analysis for NDs identification and classification.
  arXiv  Detail & Related papers  (2024-05-21T06:44:40Z)
• A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models [71.25225058845324]
  Large Language Models (LLMs) have demonstrated revolutionary abilities in language understanding and generation. Retrieval-Augmented Generation (RAG) can offer reliable and up-to-date external knowledge. RA-LLMs have emerged to harness external and authoritative knowledge bases, rather than relying on the model's internal knowledge.
  arXiv  Detail & Related papers  (2024-05-10T02:48:45Z)
• Towards Smart Healthcare: Challenges and Opportunities in IoT and ML [0.0]
  The COVID-19 pandemic and other ongoing health crises have underscored the need for prompt healthcare services worldwide. This chapter focuses exclusively on exploring the hurdles encountered when integrating machine learning methods into the IoT healthcare sector. It offers a comprehensive summary of current research challenges and potential opportunities, categorized into three scenarios.
  arXiv  Detail & Related papers  (2023-12-09T10:45:44Z)
• A Review of Reinforcement Learning for Natural Language Processing, and Applications in Healthcare [17.38031305999901]
  Reinforcement learning (RL) has emerged as a powerful approach for tackling complex medical decision-making problems. This paper presents a review of the RL techniques in NLP, highlighting key advancements, challenges, and applications in healthcare. We examined dialogue systems where RL enables the learning of conversational strategies, RL-based machine translation models, question-answering systems, text summarization, and information extraction.
  arXiv  Detail & Related papers  (2023-10-23T20:26:15Z)
• Large Language Models for Information Retrieval: A Survey [58.30439850203101]
  Information retrieval has evolved from term-based methods to its integration with advanced neural models. Recent research has sought to leverage large language models (LLMs) to improve IR systems. We delve into the confluence of LLMs and IR systems, including crucial aspects such as query rewriters, retrievers, rerankers, and readers.
  arXiv  Detail & Related papers  (2023-08-14T12:47:22Z)
• ImDrug: A Benchmark for Deep Imbalanced Learning in AI-aided Drug Discovery [79.08833067391093]
  Real-world pharmaceutical datasets often exhibit highly imbalanced distribution. We introduce ImDrug, a benchmark with an open-source Python library which consists of 4 imbalance settings, 11 AI-ready datasets, 54 learning tasks and 16 baseline algorithms tailored for imbalanced learning. It provides an accessible and customizable testbed for problems and solutions spanning a broad spectrum of the drug discovery pipeline.
  arXiv  Detail & Related papers  (2022-09-16T13:35:57Z)
• Smart Healthcare in the Age of AI: Recent Advances, Challenges, and Future Prospects [3.3336265497547126]
  The smart healthcare system is a topic of recently growing interest and has become increasingly required due to major developments in modern technologies. This paper is aimed to discuss the current state-of-the-art smart healthcare systems highlighting major areas like wearable and smartphone devices for health monitoring, machine learning for disease diagnosis, and the assistive frameworks, including social robots developed for the ambient assisted living environment.
  arXiv  Detail & Related papers  (2021-06-24T05:10:47Z)
• pH-RL: A personalization architecture to bring reinforcement learning to health practice [6.587485396428361]
  This paper presents pH-RL, a general RL architecture for personalization to bring RL to health practice. We implement our open-source RL architecture and integrate it with the MoodBuster mobile application for mental health. Our experimental results show that the developed policies learn to select appropriate actions consistently using only a few days' worth of data.
  arXiv  Detail & Related papers  (2021-03-29T19:38:04Z)
• Deep reinforcement learning in medical imaging: A literature review [17.13198095624744]
  Deep reinforcement learning (DRL) augments the reinforcement learning framework, which learns a sequence of actions that maximizes the expected reward. Recent works have demonstrated the great potential of DRL in medicine and healthcare.
  arXiv  Detail & Related papers  (2021-03-05T15:12:49Z)
• Towards Continual Reinforcement Learning: A Review and Perspectives [69.48324517535549]
  We aim to provide a literature review of different formulations and approaches to continual reinforcement learning (RL) While still in its early days, the study of continual RL has the promise to develop better incremental reinforcement learners. These include applications such as those in the fields of healthcare, education, logistics, and robotics.
  arXiv  Detail & Related papers  (2020-12-25T02:35:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.