# DeIDClinic: A Risk-Aware Pseudonymization Framework for Clinical Text De-identification and Re-identification Risk Assessment

arXiv: https://arxiv.org/abs/2410.01648 · HTML: https://mlsanity.com/paper/2410.01648/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 38 references that are in the map.
- First posted on arXiv 2 Oct 2024.

## Builds on (most cited first, 7 of 7)

- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [De-identification of Patient Notes with Recurrent Neural Networks](https://mlsanity.com/paper/1606.03475/) (2016)
- [De-identification is not enough: a comparison between de-identified and synthetic clinical notes](https://mlsanity.com/paper/2402.00179/) (2024)
- [De-identification of clinical free text using natural language processing: A systematic review of current approaches](https://mlsanity.com/paper/2312.03736/) (2023)
- [Validating transformers for redaction of text from electronic health records in real-world healthcare](https://mlsanity.com/paper/2310.04468/) (2023)
- [MASK: A flexible framework to facilitate de-identification of clinical texts](https://mlsanity.com/paper/2005.11687/) (2020)

## Built on by (most cited first, 1 of 1)

- [Institution-Specific LLM Prompting Recovers PHI That De-identification Systems and Their Gold Standards Both Miss](https://mlsanity.com/paper/2608.17051/) (2026)

## Abstract (arXiv)

The increasing availability of sensitive textual data has created an urgent need for robust de-identification methods that enable compliant data sharing while preserving downstream utility. This paper presents DeID-Clinic, a multi-layered framework for automated pseudonymization and re-identification risk assessment of clinical free-text data. Our approach integrates domain-adapted transformer models, including BioBERT and ClinicalBERT, into the MASK de-identification framework to improve the detection and masking of protected health information (PHI). Beyond entity recognition, we introduce a novel document-level risk assessment module that quantifies residual re-identification risk using a combination of k-anonymity, l-diversity, t-closeness, contextual similarity, and entity co-occurrence analysis. Experiments conducted on the i2b2 2014 de-identification dataset demonstrate strong performance, achieving macro-level F1 scores above 0.96 for several entity categories, while enabling quantitative prioritization of high-risk documents for further review. Our results highlight the effectiveness of combining neural de-identification with explicit risk modeling, supporting privacy-preserving data sharing in sensitive domains. Although evaluated on clinical text, the proposed framework is generalizable to other privacy-critical domains such as legal and administrative documents, where reliable pseudonymization and risk-aware anonymization are essential. Keywords{Automated De-Identification, Risk Assessment, Patient Privacy, Pseudonymization, Personal Health Information}