Generalizability and comparison of automatic clinical text de-identification methods and resources

In this paper, we present an evaluation of the hybrid best-of-breed automated VHA (Veteran's Health Administration) clinical text de-identification system, nicknamed BoB, developed within the VHA Consortium for Healthcare Informatics Research. We also evaluate two available machine learning-bas...

Full description

Saved in:
Bibliographic Details
Published in:AMIA ... Annual Symposium proceedings 2012, Vol.2012, p.199-208
Main Authors: Ferrández, Óscar, South, Brett R, Shen, Shuying, Friedlin, F Jeff, Samore, Matthew H, Meystre, Stéphane M
Format: Article
Language:English
Subjects:
Artificial Intelligence
Confidentiality
Electronic Health Records
Health Insurance Portability and Accountability Act
Humans
Natural Language Processing
United States
United States Department of Veterans Affairs
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, we present an evaluation of the hybrid best-of-breed automated VHA (Veteran's Health Administration) clinical text de-identification system, nicknamed BoB, developed within the VHA Consortium for Healthcare Informatics Research. We also evaluate two available machine learning-based text de-identifications systems: MIST and HIDE. Two different clinical corpora were used for this evaluation: a manually annotated VHA corpus, and the 2006 i2b2 de-identification challenge corpus. These experiments focus on the generalizability and portability of the classification models across different document sources. BoB demonstrated good recall (92.6%), satisfactorily prioritizing patient privacy, and also achieved competitive precision (83.6%) for preserving subsequent document interpretability. MIST and HIDE reached very competitive results, in most cases with high precision (92.6% and 93.6%), although recall was sometimes lower than desired for the most sensitive PHI categories.
ISSN:1559-4076