# Benchmarking Modern Named Entity Recognition Techniques for Free-text Health Record De-identification

arXiv: https://arxiv.org/abs/2103.13546 · HTML: https://mlsanity.com/paper/2103.13546/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 25 Mar 2021.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [A Survey on Deep Learning for Named Entity Recognition](https://mlsanity.com/paper/1812.09449/) (2018)
- [De-identification of Patient Notes with Recurrent Neural Networks](https://mlsanity.com/paper/1606.03475/) (2016)
- [GRN: Gated Relation Network to Enhance Convolutional Neural Network for Named Entity Recognition](https://mlsanity.com/paper/1907.05611/) (2019)
- [Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks](https://mlsanity.com/paper/1707.06799/) (2017)
- [A survey of automatic de-identification of longitudinal clinical narratives](https://mlsanity.com/paper/1810.06765/) (2018)

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

- [Pyclipse, a library for deidentification of free-text clinical notes](https://mlsanity.com/paper/2311.02748/) (2023)

## Abstract (arXiv)

Electronic Health Records (EHRs) have become the primary form of medical data-keeping across the United States. Federal law restricts the sharing of any EHR data that contains protected health information (PHI). De-identification, the process of identifying and removing all PHI, is crucial for making EHR data publicly available for scientific research. This project explores several deep learning-based named entity recognition (NER) methods to determine which method(s) perform better on the de-identification task. We trained and tested our models on the i2b2 training dataset, and qualitatively assessed their performance using EHR data collected from a local hospital. We found that 1) BiLSTM-CRF represents the best-performing encoder/decoder combination, 2) character-embeddings and CRFs tend to improve precision at the price of recall, and 3) transformers alone under-perform as context encoders. Future work focused on structuring medical text may improve the extraction of semantic and syntactic information for the purposes of EHR de-identification.