在临床研究和实践中应用于电子健康记录的自然语言处理技术 - - 对方法的介绍
Benjamin Clay1, Henry I Bergman2, Safa Salim2
1Department of Trauma and Orthopaedic Surgery, East Suffolk and North Essex NHS Foundation Trust, Ipswich Hospital, Heath Road, Ipswich, IP4 5PD, United Kingdom; Department of Public Health and Primary Care, University of Cambridge, Forvie Site, Robinson Way, Cambridge, CB2 0SR, United Kingdom.
Computers in biology and medicine
|February 13, 2025
概括
自然语言处理 (NLP) 通过自动化自由文本分析,可以改变使用电子健康记录 (EHR) 的临床研究. 本指南解释了NLP方法用于EHR数据,弥合了临床研究人员的差距.
科学领域:
- 临床信息学 临床信息学
- 计算语言学 计算语言学
背景情况:
- 电子健康记录 (EHR) 包含大量的非结构化文本数据.
- 自然语言处理 (NLP) 提供了强大的工具,可以从文本中提取有意义的信息.
- 在临床实践中NLP的有限采用凸显了需要可访问的指导.
研究的目的:
- 为临床研究人员提供关于NLP管道的全面概述,以进行EHR分析.
- 通过案例研究来证明NLP技术的实际应用.
- 弥合临床研究人员和NLP专家之间的知识差距.
主要方法:
- 介绍EHR数据的基本预处理步骤.
- 对NLP任务的统计方法和人工神经网络 (ANN) 的解释.
- 讨论信息提取和预测的监督和无监督方法.
主要成果:
- 案例研究说明了NLP在从电子健康记录中提取客观和主观信息中的应用.
- 这些例子展示了使用各种NLP方法的分类和预测任务.
- 这篇论文涵盖了最先进的大型语言模型和未来的研究方向.
结论:
- 通过释放来自EHR的见解,NLP具有革命性临床研究的巨大潜力.
- 了解NLP技术对于临床医生参与这个快速发展的领域至关重要.
- 讨论的方法预计将对未来的临床实践产生重大影响.
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