# MASK: A flexible framework to facilitate de-identification of clinical texts

arXiv: https://arxiv.org/abs/2005.11687 · HTML: https://mlsanity.com/paper/2005.11687/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2020.

## Built on by (most cited first, 3 of 3)

- [Comparison of biomedical relationship extraction methods and models for knowledge graph creation](https://mlsanity.com/paper/2201.01647/) (2022)
- [From Zero to Hero: Harnessing Transformers for Biomedical Named Entity Recognition in Zero- and Few-shot Contexts](https://mlsanity.com/paper/2305.04928/) (2023)
- [DeIDClinic: A Risk-Aware Pseudonymization Framework for Clinical Text De-identification and Re-identification Risk Assessment](https://mlsanity.com/paper/2410.01648/) (2024)

## Abstract (arXiv)

Medical health records and clinical summaries contain a vast amount of important information in textual form that can help advancing research on treatments, drugs and public health. However, the majority of these information is not shared because they contain private information about patients, their families, or medical staff treating them. Regulations such as HIPPA in the US, PHIPPA in Canada and GDPR regulate the protection, processing and distribution of this information. In case this information is de-identified and personal information are replaced or redacted, they could be distributed to the research community. In this paper, we present MASK, a software package that is designed to perform the de-identification task. The software is able to perform named entity recognition using some of the state-of-the-art techniques and then mask or redact recognized entities. The user is able to select named entity recognition algorithm (currently implemented are two versions of CRF-based techniques and BiLSTM-based neural network with pre-trained GLoVe and ELMo embedding) and masking algorithm (e.g. shift dates, replace names/locations, totally redact entity).