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English(EN) Differentially Private De-identification of Dutch Clinical Notes: A Comparative Evaluation

大型语言模型改进荷兰临床笔记去标识化的隐私-效用权衡

研究人员对荷兰临床笔记的去标识化方法进行了比较研究,以保护患者隐私并允许数据重用。该研究评估了差分隐私 (DP) 和命名实体识别 (NER) 等传统方法以及使用大型语言模型 (LLM) 的新方法。研究结果表明,单独的 DP 机制会显著降低数据效用,但将其与基于 LLM 的预处理相结合,可以在临床文本去标识化的隐私和有用性之间提供更好的平衡。 AI

影响 结合大型语言模型和差分隐私的新混合方法可能会提高去标识化临床数据在研究中的效用。

排序理由 评估临床文本去标识化隐私保护技术的学术论文。

在 arXiv cs.CL 阅读 →

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大型语言模型改进荷兰临床笔记去标识化的隐私-效用权衡

本文如何被排名

Signal score
0 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Research
评估临床文本去标识化隐私保护技术的学术论文。
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
paper, safety
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
168 days old
Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Iacer Calixto ·

    荷兰临床笔记的差分隐私去标识化:一项比较评估

    Protecting patient privacy in clinical narratives is essential for enabling secondary use of healthcare data under regulations such as GDPR and HIPAA. While manual de-identification remains the gold standard, it is costly and slow, motivating the need for automated methods that c…