title: Generalizability and comparison of automatic clinical text de-identification methods and resources.
abstract: 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.
authors.affiliation: Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
authors.forename: Óscar
authors.initials: Ó
authors.lastname: Ferrández
authors.forename: Brett R
authors.initials: BR
authors.lastname: South
authors.forename: Shuying
authors.initials: S
authors.lastname: Shen
authors.forename: F Jeff
authors.initials: FJ
authors.lastname: Friedlin
authors.forename: Matthew H
authors.initials: MH
authors.lastname: Samore
authors.forename: Stéphane M
authors.initials: SM
authors.lastname: Meystre
mesh_terms: D001185:Artificial Intelligence; D003219:Confidentiality; D057286:Electronic Health Records; D020408:Health Insurance Portability and Accountability Act; D006801:Humans; D009323:Natural Language Processing; D014481:United States; D014493:United States Department of Veterans Affairs