使用正则表达式规则和预先训练的BERT,用伪标签去识别临床笔记
Jiyong An1, Jiyun Kim1, Leonard Sunwoo2
1Graduate School of Data Science, Seoul National University, Seoul, South Korea.
BMC medical informatics and decision making
|February 18, 2025
概括
这项研究开发了一种半监督的方法去识别韩国临床笔记,结合基于规则和机器学习的方法. 综合策略显著提高了从医疗文本中删除个人信息的准确性.
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.
- 数据 隐私 数据 隐私 数据
背景情况:
- 在使用非结构化文本数据的医学研究中,对临床笔记的非识别至关重要.
- 有限的研究存在于非识别的韩国临床笔记.
- 保护电子健康记录中的患者隐私是一个日益关注的问题.
研究的目的:
- 开发和评估一种有效的韩国临床笔记的非识别方法.
- 改善在韩国医学研究中利用非结构化临床文本数据.
- 为了解决韩语语言医疗记录的非识别技术的差距.
主要方法:
- 利用了来自首尔国立大学Bundang医院的大型数据集,包括放射学和非放射学笔记.
- 采用了两阶段的非识别策略:基于规则的 (正则表达式) 和半监督的机器学习 (韩国BERT).
- 通过使用专家注释的数据来完善基于规则的方法,然后用于伪标签,以微调预先训练的韩国BERT模型.
主要成果:
- 基于规则的方法实现了97.2%的精度,93.7%的回忆率和96.2%的F1得分在放射学笔记上.
- 半监督的KoBERT-NER模型,通过伪标签数据进行微调,达到96.5%的精度,97.6%的回忆率和97.1%的F1得分.
- 验证表明,对放射学和非放射学笔记的代币级别去识别具有很高的性能.
结论:
- 结合基于规则和半监督的机器学习,提高了临床笔记去识别性能.
- 拟议的方法提供了一种有效的解决方案,用于消除韩国临床文本的识别.
- 改进的非识别功能促进了韩国医学研究中安全的数据共享和分析.
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