一种分类学,以促进系统错误分析在基于多站点电子健康记录的临床概念提取多站点电子健康记录
Sunyang Fu1,2, Liwei Wang1,2, Huan He3
1Department of AI and Informatics, Mayo Clinic, Rochester, MN 55902, United States.
概括
这项研究开发了一种标准化的错误分类学,用于临床概念提取,以改善自然语言处理 (NLP) 模型在各种医疗保健环境中的性能. 该分类学增强了多站点临床NLP研究中的可复制性和可解释性.
科学领域:
- 临床自然语言处理 (NLP)
- 医疗信息学 医疗信息学
- 医疗数据分析 医学数据分析
背景情况:
- 错误分析对于完善NLP中的临床概念提取至关重要.
- 目前的方法在标准化方面面临挑战,原因是EHR异质性.
- 手动错误审查需要NLP和领域专业知识,使复制性复杂化.
研究的目的:
- 为临床概念提取错误建立共同的定义和分类学.
- 促进社区对错误分析程序的共识.
- 提高NLP错误分析的标准化和可重复性.
主要方法:
- 错误分类学的代开发和评估.
- 纳入现有文献,标准,现实数据和多站点评估.
- 社区反和发布分类学在 .dtd 和 .owl 格式与注释工具兼容.
主要成果:
- 创建了一个分类系统,包含43个错误类,跨越6个维度和4个属性.
- 评估显示出基于方法,任务和EHR设置的错误类型的显著差异.
- 社区反强调了需要提高清晰度,通用性,可用性和传播的必要性.
结论:
- 拟议的分类学标准化和加速临床NLP的多站点错误分析.
- 它提高了NLP模型的来源,可解释性和可移植性.
- 未来的工作可能会集中在开发错误分类和标准化的自动化方法上.
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