pyDeid:一种改进的,快速的,灵活的和可泛化的基于规则的方法,用于免费文本医疗记录的非识别
Vaakesan Sundrelingam1, Shireen Parimoo2, Frances Pogacar3
1Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, ON M5B 1T8, Canada.
JAMIA open
|January 23, 2025
概括
一个新的Python工具,pyDeid,增强了临床数据的非识别. 与现有方法相比,它提供了更高的准确性和速度,提高了研究数据的可用性.
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:
- 确定临床文本对于研究数据共享至关重要.
- 现有的消除身份工具存在功能缺陷和低于最佳准确性和速度的权衡.
- 需要灵活和高效的去识别解决方案.
研究的目的:
- 介绍pyDeid,这是一个基于Python的新型身份删除软件.
- 解决当前非识别工具中的局限性.
- 提高临床文本识别的准确性,速度和灵活性.
主要方法:
- pyDeid使用正规表达式,排除/包含列表和可选的命名实体识别.
- 根据加拿大医院笔记,n2c2数据集和合成AI生成的笔记进行评估.
- 性能与Physionet 识别软件和Philter相比较.
主要成果:
- pyDeid在各种指标上取得了卓越的性能,包括0.988.8的最佳精度.
- 获得了最好的精度 (0.889),回忆 (0.950) 和F1得分 (0.904).
- 演示了显著更快的脱标速度,最快的速度为每笔记0.48秒.
结论:
- pyDeid提供灵活性来平衡性能,速度,精度和回忆.
- 解决现有非识别工具的功能缺口和权衡问题.
- 超出临床数据的概括性,可针对各种应用进行定制.
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