微调OpenDeLD无标识管道的初步评估 跨多中心的Corpora
Shalini Gupta1, Jiaxing Liu2, Zoie Shiu-Yee Wong3,4
1CGD Health Pvt. Ltd. Hyderabad, Telangana, India.
Studies in health technology and informatics
|August 23, 2024
概括
这项研究评估了电子健康记录 (EHR) 的混合脱身份策略,在保护生物医学研究的患者隐私方面取得了高绩效.
科学领域:
- 医疗信息学 医疗信息学
- 生物医学研究生物医学研究
- 数据 隐私 数据 隐私 数据
背景情况:
- 电子健康记录 (EHR) 的自动去识别对于生物医学研究中的二次数据使用至关重要.
- 在使用敏感健康信息时,必须保护患者的隐私.
- 现有的去识别方法需要对各种数据集进行评估.
研究的目的:
- 通过使用OpenDeID管道来评估混合脱标策略.
- 评估管道在各种EHR公司的有效性.
- 在二次EHR使用中增强患者隐私.
主要方法:
- 使用了三个不同的体:OpenDeID v2 (病理学),i2b2/UTHealth 2014 (临床叙述),以及CEGS N-GRID 2016 (精神病学笔记).
- 在OpenDeID管道中采用混合式去识别方法,将深度学习和上下文规则结合起来.
- 预处理数据以协调编码和格式问题.
- 使用精度,回忆和F测量指标评估性能.
主要成果:
- 排放总结 生物BERT模型显示出优异的身份删除性能.
- 最好的模型,在4038个机构中训练了4038份报告,实现了微平均F1分数0.9248 (严格) 和0.9692 (放松).
- 混合战略在保护电子健康记录数据集中的敏感信息方面被证明是有效的.
结论:
- 经过评估的混合型去识别策略,特别是放电摘要 BioBERT 模型,显示出显著的有效性.
- 这种方法提供了一个强大的解决方案,用于保护患者的隐私在二次EHR数据利用.
- 这些发现为在生物医学研究中实施安全的EHR去识别提供了有价值的见解.
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