A Review of Reinforcement Learning for Natural Language Processing, and Applications in Healthcare

• URL: http://arxiv.org/abs/2310.18354v1
• Date: Mon, 23 Oct 2023 20:26:15 GMT
• Title: A Review of Reinforcement Learning for Natural Language Processing, and Applications in Healthcare
• Authors: Ying Liu, Haozhu Wang, Huixue Zhou, Mingchen Li, Yu Hou, Sicheng Zhou, Fang Wang, Rama Hoetzlein, Rui Zhang
• Abstract summary: 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.
• Score: 17.38031305999901
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement learning (RL) has emerged as a powerful approach for tackling complex medical decision-making problems such as treatment planning, personalized medicine, and optimizing the scheduling of surgeries and appointments. It has gained significant attention in the field of Natural Language Processing (NLP) due to its ability to learn optimal strategies for tasks such as dialogue systems, machine translation, and question-answering. This paper presents a review of the RL techniques in NLP, highlighting key advancements, challenges, and applications in healthcare. The review begins by visualizing a roadmap of machine learning and its applications in healthcare. And then it explores the integration of RL with NLP tasks. 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. Additionally, ethical considerations and biases in RL-NLP systems are addressed.

Related papers

• Demystifying Large Language Models for Medicine: A Primer [50.83806796466396]
  Large language models (LLMs) represent a transformative class of AI tools capable of revolutionizing various aspects of healthcare. This tutorial aims to equip healthcare professionals with the tools necessary to effectively integrate LLMs into clinical practice.
  arXiv  Detail & Related papers  (2024-10-24T15:41:56Z)
• Procedure-Aware Surgical Video-language Pretraining with Hierarchical Knowledge Augmentation [51.222684687924215]
  Surgical video-language pretraining faces unique challenges due to the knowledge domain gap and the scarcity of multi-modal data. We propose a hierarchical knowledge augmentation approach and a novel Procedure-Encoded Surgical Knowledge-Augmented Video-Language Pretraining framework to tackle these issues.
  arXiv  Detail & Related papers  (2024-09-30T22:21:05Z)
• Diagnostic Reasoning in Natural Language: Computational Model and Application [68.47402386668846]
  We investigate diagnostic abductive reasoning (DAR) in the context of language-grounded tasks (NL-DAR) We propose a novel modeling framework for NL-DAR based on Pearl's structural causal models. We use the resulting dataset to investigate the human decision-making process in NL-DAR.
  arXiv  Detail & Related papers  (2024-09-09T06:55:37Z)
• Dr-LLaVA: Visual Instruction Tuning with Symbolic Clinical Grounding [53.629132242389716]
  Vision-Language Models (VLM) can support clinicians by analyzing medical images and engaging in natural language interactions. VLMs often exhibit "hallucinogenic" behavior, generating textual outputs not grounded in contextual multimodal information. We propose a new alignment algorithm that uses symbolic representations of clinical reasoning to ground VLMs in medical knowledge.
  arXiv  Detail & Related papers  (2024-05-29T23:19:28Z)
• ChatGPT-HealthPrompt. Harnessing the Power of XAI in Prompt-Based Healthcare Decision Support using ChatGPT [15.973406739758856]
  This study presents an innovative approach to the application of large language models (LLMs) in clinical decision-making, focusing on OpenAI's ChatGPT. Our approach introduces the use of contextual prompts-strategically designed to include task description, feature description, and crucially, integration of domain knowledge-for high-quality binary classification tasks even in data-scarce scenarios.
  arXiv  Detail & Related papers  (2023-08-17T20:50:46Z)
• 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)
• Are Large Language Models Ready for Healthcare? A Comparative Study on Clinical Language Understanding [12.128991867050487]
  Large language models (LLMs) have made significant progress in various domains, including healthcare. In this study, we evaluate state-of-the-art LLMs within the realm of clinical language understanding tasks.
  arXiv  Detail & Related papers  (2023-04-09T16:31:47Z)
• Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Survey [42.17523380108375]
  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.
  arXiv  Detail & Related papers  (2021-08-05T18:47:17Z)
• Survey on reinforcement learning for language processing [17.738843098424816]
  This paper reviews the state of the art of reinforcement learning methods for different problems of natural language processing. We provide detailed descriptions of the problems as well as discussions of why RL is well-suited to solve them. We elaborate on promising research directions in natural language processing that might benefit from reinforcement learning.
  arXiv  Detail & Related papers  (2021-04-12T15:33:11Z)
• 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)
• A Systematic Review of Natural Language Processing for Knowledge Management in Healthcare [0.6193838300896449]
  The objective of this paper is to identify the potential of NLP, especially, how NLP is used to support the knowledge management process in the healthcare domain. This paper provides a comprehensive survey of the state-of-the-art NLP research with a particular focus on how knowledge is created, captured, shared, and applied in the healthcare domain.
  arXiv  Detail & Related papers  (2020-07-17T17:50:50Z)

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.