Generalizability and comparison of automatic clinical text de-identification methods and resources.
Year: 2012
Type: article
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.
Source: PubMed Central
Authors: Óscar Ferrández, Brett R South, Shuying Shen, F Jeff Friedlin, Matthew H Samore, Stéphane M Meystre
Institution: University of Utah
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1481556729