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.
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.
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.
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