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一个可扩展的评估框架应用于临床文本识别自然语言处理工具:多系统和多体研究.
Paul M Heider1, Stéphane M Meystre2
1Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC, United States.
Journal of medical Internet research
|May 28, 2024
概括
这项研究引入了一个开源框架,用于比较临床自然语言处理 (NLP) 识别系统. 该框架揭示了显著的性能差异和权衡,旨在推进系统评估和开发.
科学领域:
- 计算语言学计算语言学
- 医疗信息学医学信息学
- 软件工程 软件工程是软件工程.
背景情况:
- 临床自然语言处理 (NLP) 缺乏对去识别系统的标准化评估框架.
- 目前的方法在不同的公司和个人身份信息 (PII) 类别中提供了有限的可比性.
- 研究人员需要一个统一的系统来一致评估NLP的性能.
研究的目的:
- 提出一个开源的,可扩展的框架,用于比较临床NLP脱标系统的性能.
- 为了使跨语料库的评估,即使与非对齐的注释类别.
- 为了方便在一个一致的评估管道中更容易测试新系统和corpora.
主要方法:
- 开发了一个端到端的框架,使用外脚本来实现可扩展性.
- 评估了六个现成的去识别系统 (CliniDeID,deid,MIST,NeuroNER,NLM Scrubber,Philter) 进行了评估.
- 在三个具有非模拟注释类别的临床文本体中测试了系统,包括私人和公共数据集.
主要成果:
- 观察到处理速度的大量变化,MIST是最快的 (24.57音符/秒),CliniDeID是最慢的 (1.00音符/秒).
- 没有一个单一的系统始终优于其他系统;在PII类别和企业中,性能各不相同.
- 确定了精度和召回之间的权衡,CliniDeID和Philter赞成召回,而其他人则赞成精度.
结论:
- 在隔离中评估NLP和去识别系统的极限比较分析.
- 跨多个系统和机构的统一评估管道可以进行更细致的比较.
- 这个开放的管道旨在降低NLP系统评估和进步的障碍.


