# De-identification of medical records using conditional random fields and long short-term memory networks

arXiv: https://arxiv.org/abs/1709.06901 · HTML: https://mlsanity.com/paper/1709.06901/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2017.

## Built on by (most cited first, 5 of 5)

- [De-identification of clinical free text using natural language processing: A systematic review of current approaches](https://mlsanity.com/paper/2312.03736/) (2023)
- [De-identification of Privacy-related Entities in Job Postings](https://mlsanity.com/paper/2105.11223/) (2021)
- [Towards Automatic Evaluation and Selection of PHI De-identification Models via Multi-Agent Collaboration](https://mlsanity.com/paper/2510.16194/) (2025)
- [De-identifying Australian Hospital Discharge Summaries: An End-to-End Framework using Ensemble of Deep Learning Models](https://mlsanity.com/paper/2101.00146/) (2021)
- [Synthetic Data for Veterinary EHR De-identification: Benefits, Limits, and Safety Trade-offs Under Fixed Compute](https://mlsanity.com/paper/2601.09756/) (2026)

## Abstract (arXiv)

The CEGS N-GRID 2016 Shared Task 1 in Clinical Natural Language Processing focuses on the de-identification of psychiatric evaluation records. This paper describes two participating systems of our team, based on conditional random fields (CRFs) and long short-term memory networks (LSTMs). A pre-processing module was introduced for sentence detection and tokenization before de-identification. For CRFs, manually extracted rich features were utilized to train the model. For LSTMs, a character-level bi-directional LSTM network was applied to represent tokens and classify tags for each token, following which a decoding layer was stacked to decode the most probable protected health information (PHI) terms. The LSTM-based system attained an i2b2 strict micro-F_1 measure of 89.86%, which was higher than that of the CRF-based system.