# Feature-Augmented Neural Networks for Patient Note De-identification

arXiv: https://arxiv.org/abs/1610.09704 · HTML: https://mlsanity.com/paper/1610.09704/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2016.

## Built on by (most cited first, 4 of 4)

- [Memory Classifiers: Two-stage Classification for Robustness in Machine Learning](https://mlsanity.com/paper/2206.05323/) (2022)
- [Assessing mortality prediction through different representation models based on concepts extracted from clinical notes](https://mlsanity.com/paper/2207.10872/) (2022)
- [Secondary Use of Clinical Problem List Entries for Neural Network-Based Disease Code Assignment](https://mlsanity.com/paper/2112.13756/) (2021)
- [Applying unsupervised keyphrase methods on concepts extracted from discharge sheets](https://mlsanity.com/paper/2303.08928/) (2023)

## Abstract (arXiv)

Patient notes contain a wealth of information of potentially great interest to medical investigators. However, to protect patients' privacy, Protected Health Information (PHI) must be removed from the patient notes before they can be legally released, a process known as patient note de-identification. The main objective for a de-identification system is to have the highest possible recall. Recently, the first neural-network-based de-identification system has been proposed, yielding state-of-the-art results. Unlike other systems, it does not rely on human-engineered features, which allows it to be quickly deployed, but does not leverage knowledge from human experts or from electronic health records (EHRs). In this work, we explore a method to incorporate human-engineered features as well as features derived from EHRs to a neural-network-based de-identification system. Our results show that the addition of features, especially the EHR-derived features, further improves the state-of-the-art in patient note de-identification, including for some of the most sensitive PHI types such as patient names. Since in a real-life setting patient notes typically come with EHRs, we recommend developers of de-identification systems to leverage the information EHRs contain.