Problem
Research questions and friction points this paper is trying to address.
clinical notes
personally identifiable information
data reuse
on-premises
de-identification
MedDeID enables locally governed clinical-text de-identification from real or synthetic training data
🤖 AI Summary
MedDeID通过结合内部标注、合成笔记生成及模型训练等方法，解决了临床笔记中个人身份信息保护的问题，实现本地治理的去标识化。
📝 Abstract
Clinical notes contain personally identifiable information (PII), restricting reuse for research and medical AI, especially when data cannot leave an institution. We developed MedDeID, an on-premises framework combining in-house annotation and synthetic-note generation with model training, inference, pseudonymisation and evaluation. On an independently annotated, adjudicated 300-note Dutch hospital benchmark, a hospital-trained compact transformer detected 98.9% of identifying text while redacting 0.24% of text outside annotated identifiers; a synthetic-only counterpart detected 96.1%. On 100 primary-care notes, the synthetic-trained model achieved higher recall than the hospital-trained model (90.3% versus 87.0%) and greater robustness to identifier-format perturbations. An English instantiation trained without real text detected 99.7% and 98.9% of annotated identifier characters on two external synthetic benchmarks. These results demonstrate transfer of the workflow to another language, but not clinical English performance. MedDeID provides a route to locally governed de-identification using real or synthetic training data.
Innovation
Methods, ideas, or system contributions that make the work stand out.
locally governed
clinical-text de-identification
synthetic data
transformer model
pseudonymisation
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