医学错误:在临床概念提取中进行系统错误分析的机器辅助框架
Hongfang Liu1, Sunyang Fu2, Qiuhao Lu2
1University of Texas Health Science Center at Houston.
Research square
|September 26, 2025
概括
一个新的框架,MedError,标准化了用于临床概念提取的错误分析. 这种机器辅助的人在循环系统改善了临床自然语言处理模型的评估.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 临床数据科学 临床数据科学
背景情况:
- 错误分析对于临床概念提取模型至关重要,这是临床自然语言处理 (NLP) 中的一个关键任务.
- 目前的错误分析缺乏标准化,需要专家判断,并阻碍可重复性.
- 临床文本的变化使模型评估复杂化.
研究的目的:
- 开发和验证MedError,一个用于临床概念提取中的系统和增强错误分析的新型框架.
- 通过机器辅助的人在循环方法来标准化评估临床NLP模型的过程.
主要方法:
- 在三家医院收集和整理了4,237份临床笔记中的1,187个独特错误.
- 使用经过验证的分类学定义错误类别,分类480个错误负数和707个错误正数.
- 评估了用于自动错误分类的大型语言模型 (LLM),并开发了具有用户友好的界面的MedError框架.
主要成果:
- MedError集成了LLM辅助的分类和推理,以进行高效,可重复和上下文意识的错误分析.
- 该框架支持单站点和联合多站点分析.
- 在25种类型和48种临床概念类别中成功分类错误.
结论:
- MedError提供了一个标准化,机器辅助的框架,以增强临床概念提取错误分析.
- 该系统有助于在现实世界医疗保健环境中有效部署临床NLP工具.
- 改善临床NLP模型的评估和改进.
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