PHICON: Improving Generalization of Clinical Text De-identification Models via Data Augmentation

Xiang Yue, Shuang Zhou


Abstract
De-identification is the task of identifying protected health information (PHI) in the clinical text. Existing neural de-identification models often fail to generalize to a new dataset. We propose a simple yet effective data augmentation method PHICON to alleviate the generalization issue. PHICON consists of PHI augmentation and Context augmentation, which creates augmented training corpora by replacing PHI entities with named-entities sampled from external sources, and by changing background context with synonym replacement or random word insertion, respectively. Experimental results on the i2b2 2006 and 2014 de-identification challenge datasets show that PHICON can help three selected de-identification models boost F1-score (by at most 8.6%) on cross-dataset test setting. We also discuss how much augmentation to use and how each augmentation method influences the performance.
Anthology ID:
2020.clinicalnlp-1.23
Volume:
Proceedings of the 3rd Clinical Natural Language Processing Workshop
Month:
November
Year:
2020
Address:
Online
Venues:
ClinicalNLP | EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
209–214
Language:
URL:
https://www.aclweb.org/anthology/2020.clinicalnlp-1.23
DOI:
10.18653/v1/2020.clinicalnlp-1.23
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.clinicalnlp-1.23.pdf