Feature-Augmented Neural Networks for Patient Note De-identification

Ji Young Lee, Franck Dernoncourt, Özlem Uzuner, Peter Szolovits


Abstract
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.
Anthology ID:
W16-4204
Volume:
Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP)
Month:
December
Year:
2016
Address:
Osaka, Japan
Venues:
ClinicalNLP | WS
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
17–22
Language:
URL:
https://aclanthology.org/W16-4204
DOI:
Bibkey:
Cite (ACL):
Ji Young Lee, Franck Dernoncourt, Özlem Uzuner, and Peter Szolovits. 2016. Feature-Augmented Neural Networks for Patient Note De-identification. In Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP), pages 17–22, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
Feature-Augmented Neural Networks for Patient Note De-identification (Lee et al., 2016)
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PDF:
https://aclanthology.org/W16-4204.pdf