Department of Surgery, University of Minnesota, Minneapolis, MN 55455, United States.
Amazon Web Service, Seattle, WA 98109, United States.
J Am Med Inform Assoc. 2024 Oct 1;31(10):2379-2393. doi: 10.1093/jamia/ocae215.
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.
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.
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.
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.
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.
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.
强化学习 (RL) 是自然语言处理 (NLP) 中的一个重要途径，为在任务完成中获得最佳策略提供了一种强大的机制。这项文献综述研究了 RL 在各种 NLP 应用中表现出的有效性，特别是在医疗保健环境中的应用。
系统地探索 RL 在 NLP 中的应用，重点关注其在获得最佳策略方面的有效性，特别是在医疗保健环境中，并全面了解 RL 在 NLP 任务中的潜力。
根据 PRISMA 指南，进行了全面的文献综述，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、问答、文本摘要和信息提取。我们的方法涉及仔细检查这些应用中使用的 RL 方法的技术方面，分析算法、状态、奖励、动作、数据集和编码器-解码器架构。
对 93 篇论文的综述深入了解了 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、使用的数据集以及 RL-NLP 系统的动态领域，从而为 RL 和机器学习技术在医疗保健中的研究和开发提供了路线图。该综述还解决了伦理问题，以确保在 RL 为基础的 NLP 技术的发展和应用中公平、透明和问责制，特别是在医疗保健等敏感领域。
研究结果强调了 RL 在推进 NLP 应用方面的有前途的作用，特别是在医疗保健领域，它在优化决策和改善患者结果方面具有巨大的潜力。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。
通过系统地探索 RL 在 NLP 中的应用，并提供技术分析、伦理影响和潜在进展的见解，该综述为深入了解 RL 在语言处理中的作用做出了贡献。
