支持循证医学的自然语言处理：一项范围综述
Natural Language Processing in Support of Evidence-based Medicine: A Scoping Review
AI总结：
本综述回顾了129项利用NLP支持循证医学（EBM）的研究，系统探讨NLP如何辅助EBM的五个基本步骤，并指出当前局限与未来方向，强调NLP在改进证据提取、综合、评估与总结以提升临床工作流程方面的变革潜力。
摘要：
循证医学（EBM）处于现代医疗保健的前沿，强调使用现有最佳科学证据来指导临床决策。由于医学文献数量庞大且增长迅速，加之整理成本高昂，迫切需要研究自然语言处理（NLP）方法，以在EBM中识别、评估、综合、总结和传播证据。本综述对129项利用NLP支持EBM的研究进行了深入回顾，阐明了其在增强临床决策过程中的关键作用。本文系统地探讨了NLP如何支持EBM的五个基本步骤——提问、获取、评估、应用和分析。该综述不仅指出了该领域当前的局限性，还提出了未来研究的方向，强调NLP有望通过改进证据提取、证据综合、评估、总结，增强数据可理解性，并促进更高效的临床工作流程来彻底变革EBM。
英文摘要：
Evidence-based medicine (EBM) is at the forefront of modern healthcare, emphasizing the use of the best available scientific evidence to guide clinical decisions. Due to the sheer volume and rapid growth of medical literature and the high cost of curation, there is a critical need to investigate Natural Language Processing (NLP) methods to identify, appraise, synthesize, summarize, and disseminate evidence in EBM. This survey presents an in-depth review of 129 research studies on leveraging NLP for EBM, illustrating its pivotal role in enhancing clinical decision-making processes. The paper systematically explores how NLP supports the five fundamental steps of EBM -- Ask, Acquire, Appraise, Apply, and Assess. The review not only identifies current limitations within the field but also proposes directions for future research, emphasizing the potential for NLP to revolutionize EBM by refining evidence extraction, evidence synthesis, appraisal, summarization, enhancing data comprehensibility, and facilitating a more efficient clinical workflow.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
