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Effective communication among healthcare professionals during hand-off reporting is essential to delivering safe and continuous patient care. Common professional interactions include reports to healthcare team members, hand-off, and transfer reports. Nurses routinely report information to other healthcare team members and also urgently contact healthcare providers to report changes in patient status.
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In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...
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相关实验视频

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使用基于提示的机器阅读理解的临床概念和关系提取.

Cheng Peng1, Xi Yang1,2, Zehao Yu1

  • 1Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.

Journal of the American Medical Informatics Association : JAMIA
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PubMed
概括

一个新的统一的基于提示的机器阅读理解 (MRC) 系统增强了临床概念和关系提取. 这种自然语言处理方法在各个机构中显示出卓越的性能和通用性.

关键词:
临床概念提取 临床概念提取机器阅读理解 阅读理解自然语言处理自然语言处理.关系提取关系提取变压器模型变压器模型

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科学领域:

  • 自然语言处理自然语言处理.
  • 临床信息学 临床信息学
  • 机器学习 机器学习

背景情况:

  • 临床概念和关系提取对于生物医学研究和临床决策支持至关重要.
  • 现有的方法通常需要单独的模型来提取概念和关系,从而限制了效率和通用性.
  • 开发具有强大的跨机构绩效的统一系统仍然是一个挑战.

研究的目的:

  • 开发一个统一的基于提示的机器阅读理解 (MRC) 架构,用于临床概念和关系提取.
  • 评估拟议的MRC模型在跨机构环境中的通用性和转移学习能力.
  • 将MRC模型的性能与临床NLP任务的现有深度学习方法进行比较.

主要方法:

  • 使用统一的基于提示的MRC架构制定了临床概念和关系提取.
  • 探索了最先进的变压器模型,包括GatorTron-MRC和BERT-MIMIC-MRC.
  • 从国家NLP临床挑战 (n2c2) 2018年和2022年的基准数据集评估模型,包括跨机构转移学习.
  • 进行错误分析,并检查不同提示策略的影响.

主要成果:

  • 拟议的MRC模型在临床概念和关系提取任务上实现了最先进的性能.
  • 在概念提取方面,GatorTron-MRC表现出优异的F1分数,表现比以前的模型优于1%-3%.
  • 在关系提取方面,GatorTron-MRC和BERT-MIMIC-MRC获得了最高的F1分数,表现比之前的模型高0.9%-11%.
  • 在跨机构评估中,MRC模型显示了显著的改进,GatorTron-MRC高达16%的表现优于传统的GatorTron.

结论:

  • 统一的基于提示的MRC架构为临床概念和关系提取提供了强大的和可概括的方法.
  • 拟议的模型在处理复杂的注释方面表现出增强的能力,并显示出强大的可移植性,用于跨机构的应用.
  • 开发的临床MRC包是公开的,促进了临床NLP的进一步研究和应用.