Problem
Research questions and friction points this paper is trying to address.
Personally Identifiable Information
Clinical De-identification
Synthetic Datasets
Meddies-PII: A Multilingual Framework for Personally Identifiable Information Extraction in Clinical De-identification
🤖 AI Summary
本文通过构建包含17种语言的百万级合成临床文档数据集Meddies-PII-Dataset，并训练BIOES标记分类器模型Meddies-PII-Model，有效提升了多语言环境下个人身份信息提取的准确性。
📝 Abstract
Clinical de-identification relies on accurately identifying personally identifiable information (PII). However, manually annotated datasets are costly to construct, while existing synthetic alternatives often provide limited details about their generation process or rely on relatively simple synthesis strategies. We introduce Meddies-PII-Dataset, a corpus of one million synthetic clinical documents spanning seventeen languages and nine PII labels. The documents are generated using attribute-conditioned prompts and validated through thirteen deterministic gates that enforce structural and annotation consistency. To evaluate the dataset's utility, we train Meddies-PII-Model, a BIOES token classifier, and compare it with existing PII extraction systems using exact-match entity-level F1. Meddies-PII-Model achieves the highest performance among the evaluated systems on all reported benchmarks, with a mean F1 of 0.827 across fifteen external benchmarks, compared with 0.658 for the strongest baseline. Upon acceptance, we will publicly release the dataset, benchmark suite, model, generation framework, and evaluation code to support research on multilingual clinical de-identification.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Multilingual Framework
Personally Identifiable Information Extraction
Clinical De-identification
Synthetic Dataset
BIOES Token Classifier
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