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

Ferrández Ó, South BR, Shen S, Friedlin FJ, Samore MH, Meystre SM. Generalizability and comparison of automatic clinical text de-identification methods and resources. AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium. 2013 Jul 30; 2012:199-208.

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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.





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