# A Deep Learning Architecture for De-identification of Patient Notes: Implementation and Evaluation

arXiv: https://arxiv.org/abs/1810.01570 · HTML: https://mlsanity.com/paper/1810.01570/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2018.

## Built on by (most cited first, 6 of 6)

- [De-identification of Privacy-related Entities in Job Postings](https://mlsanity.com/paper/2105.11223/) (2021)
- [Beyond Accuracy: Automated De-Identification of Large Real-World Clinical Text Datasets](https://mlsanity.com/paper/2312.08495/) (2023)
- [De-identification of Unstructured Clinical Texts from Sequence to Sequence Perspective](https://mlsanity.com/paper/2108.07971/) (2021)
- [Large Language Model Empowered Privacy-Protected Framework for PHI Annotation in Clinical Notes](https://mlsanity.com/paper/2504.18569/) (2025)
- [End-to-end speech recognition modeling from de-identified data](https://mlsanity.com/paper/2207.05469/) (2022)
- [Can Zero-Shot Commercial APIs Deliver Regulatory-Grade Clinical Text DeIdentification?](https://mlsanity.com/paper/2503.20794/) (2025)

## Abstract (arXiv)

De-identification is the process of removing 18 protected health information (PHI) from clinical notes in order for the text to be considered not individually identifiable. Recent advances in natural language processing (NLP) has allowed for the use of deep learning techniques for the task of de-identification. In this paper, we present a deep learning architecture that builds on the latest NLP advances by incorporating deep contextualized word embeddings and variational drop out Bi-LSTMs. We test this architecture on two gold standard datasets and show that the architecture achieves state-of-the-art performance on both data sets while also converging faster than other systems without the use of dictionaries or other knowledge sources.