CRFs based de-identification of medical records.

Journal of Biomedical Informatics
Bin HeWenlan Hua

Abstract

De-identification is a shared task of the 2014 i2b2/UTHealth challenge. The purpose of this task is to remove protected health information (PHI) from medical records. In this paper, we propose a novel de-identifier, WI-deId, based on conditional random fields (CRFs). A preprocessing module, which tokenizes the medical records using regular expressions and an off-the-shelf tokenizer, is introduced, and three groups of features are extracted to train the de-identifier model. The experiment shows that our system is effective in the de-identification of medical records, achieving a micro-F1 of 0.9232 at the i2b2 strict entity evaluation level.

References

Jun 30, 2007·Journal of the American Medical Informatics Association : JAMIA·Ozlem UzunerPeter Szolovits
Jun 30, 2007·Journal of the American Medical Informatics Association : JAMIA·Ben WellnerLynette Hirschman
Dec 7, 2007·Artificial Intelligence in Medicine·Ozlem UzunerPeter Szolovits
Jul 26, 2008·BMC Medical Informatics and Decision Making·Ishna NeamatullahGari D Clifford
Aug 4, 2010·BMC Medical Research Methodology·Stephane M MeystreMatthew H Samore
May 6, 2014·Artificial Intelligence in Medicine·Guido ZucconAnton Bergheim
May 27, 2014·Journal of Biomedical Informatics·Xinbo LvBenyang Deng
Sep 1, 2015·Journal of Biomedical Informatics·Amber Stubbs, Özlem Uzuner
Oct 31, 2015·Journal of Biomedical Informatics·Özlem Uzuner, Amber Stubbs

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Citations

Oct 31, 2015·Journal of Biomedical Informatics·Özlem Uzuner, Amber Stubbs
Jul 13, 2017·BMC Medical Informatics and Decision Making·Zengjian LiuHua Xu
Jun 6, 2017·Journal of Biomedical Informatics·Zengjian LiuQingcai Chen
Oct 17, 2017·Journal of Biomedical Informatics·Zhipeng JiangJingchi Jiang

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Software Mentioned

deId
WI
Dic
OpenNLP
GENIA
LOCATION
CRF

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