回顾强化学习在自然语言处理和医疗保健中的应用。

IF 4.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Ying Liu, Haozhu Wang, Huixue Zhou, Mingchen Li, Yu Hou, Sicheng Zhou, Fang Wang, Rama Hoetzlein, Rui Zhang
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引用次数: 0

摘要

重要性:强化学习(RL)是自然语言处理(NLP)中的一个重要途径,它提供了一种在完成任务过程中获得最佳策略的有效机制。这篇文献综述研究了强化学习在 NLP 中的各种应用,其中强化学习在医疗保健领域的应用效果显著:系统探索 RL 在 NLP 中的应用,重点关注其在获取最佳策略方面的有效性,尤其是在医疗保健领域的应用,并全面了解 RL 在 NLP 任务中的潜力:根据 PRISMA 准则,我们进行了详尽的文献综述,以确定 RL 在 NLP 应用中取得成功的实例,包括对话系统、机器翻译、问题解答、文本摘要和信息提取。我们的方法包括仔细研究这些应用中采用的 RL 方法的技术方面,分析算法、状态、奖励、操作、数据集和编码器-解码器架构:通过对 93 篇论文的综述,我们深入了解了 RL 算法、流行技术、新兴趋势以及 RL 方法在 NLP 医疗保健应用中的融合。它阐明了所采用的战略方法、利用的数据集以及 RL-NLP 系统的动态范围,从而为医疗保健领域的 RL 和机器学习技术的研究与开发提供了路线图。该综述还探讨了伦理问题,以确保基于 RL 的 NLP 技术在发展和应用过程中的公平性、透明度和问责制,尤其是在医疗保健等敏感领域:讨论:研究结果强调了 RL 在推进 NLP 应用方面的重要作用,尤其是在医疗保健领域,因为它在优化决策和提高患者治疗效果方面具有巨大潜力。然而,与 RL 相关的伦理挑战和技术复杂性需要仔细考虑和持续研究,以确保负责任和有效的实施:本综述系统地探讨了 RL 在 NLP 中的应用,并对技术分析、伦理影响和潜在进步提出了见解,有助于加深对 RL 在语言处理中的作用的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A review of reinforcement learning for natural language processing and applications in healthcare.

Importance: Reinforcement learning (RL) represents a pivotal avenue within natural language processing (NLP), offering a potent mechanism for acquiring optimal strategies in task completion. This literature review studies various NLP applications where RL has demonstrated efficacy, with notable applications in healthcare settings.

Objectives: To systematically explore the applications of RL in NLP, focusing on its effectiveness in acquiring optimal strategies, particularly in healthcare settings, and provide a comprehensive understanding of RL's potential in NLP tasks.

Materials and methods: Adhering to the PRISMA guidelines, an exhaustive literature review was conducted to identify instances where RL has exhibited success in NLP applications, encompassing dialogue systems, machine translation, question-answering, text summarization, and information extraction. Our methodological approach involves closely examining the technical aspects of RL methodologies employed in these applications, analyzing algorithms, states, rewards, actions, datasets, and encoder-decoder architectures.

Results: The review of 93 papers yields insights into RL algorithms, prevalent techniques, emergent trends, and the fusion of RL methods in NLP healthcare applications. It clarifies the strategic approaches employed, datasets utilized, and the dynamic terrain of RL-NLP systems, thereby offering a roadmap for research and development in RL and machine learning techniques in healthcare. The review also addresses ethical concerns to ensure equity, transparency, and accountability in the evolution and application of RL-based NLP technologies, particularly within sensitive domains such as healthcare.

Discussion: The findings underscore the promising role of RL in advancing NLP applications, particularly in healthcare, where its potential to optimize decision-making and enhance patient outcomes is significant. However, the ethical challenges and technical complexities associated with RL demand careful consideration and ongoing research to ensure responsible and effective implementation.

Conclusions: By systematically exploring RL's applications in NLP and providing insights into technical analysis, ethical implications, and potential advancements, this review contributes to a deeper understanding of RL's role for language processing.

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来源期刊
Journal of the American Medical Informatics Association
Journal of the American Medical Informatics Association 医学-计算机:跨学科应用
CiteScore
14.50
自引率
7.80%
发文量
230
审稿时长
3-8 weeks
期刊介绍: JAMIA is AMIA''s premier peer-reviewed journal for biomedical and health informatics. Covering the full spectrum of activities in the field, JAMIA includes informatics articles in the areas of clinical care, clinical research, translational science, implementation science, imaging, education, consumer health, public health, and policy. JAMIA''s articles describe innovative informatics research and systems that help to advance biomedical science and to promote health. Case reports, perspectives and reviews also help readers stay connected with the most important informatics developments in implementation, policy and education.
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