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De-identification of medical records using conditional random fields and long short-term memory networks

2017-09-29 12:03:57
Zhipeng Jiang, Chao Zhao, Bin He, Yi Guan, Jingchi Jiang
arXiv_CV
         
Abstract

The CEGS N-GRID 2016 Shared Task 1 in Clinical Natural Language Processing focuses on the de-identification of psychiatric evaluation records. This paper describes two participating systems of our team, based on conditional random fields (CRFs) and long short-term memory networks (LSTMs). A pre-processing module was introduced for sentence detection and tokenization before de-identification. For CRFs, manually extracted rich features were utilized to train the model. For LSTMs, a character-level bi-directional LSTM network was applied to represent tokens and classify tags for each token, following which a decoding layer was stacked to decode the most probable protected health information (PHI) terms. The LSTM-based system attained an i2b2 strict micro-F_1 measure of 89.86%, which was higher than that of the CRF-based system.

Abstract (translated)

URL

https://arxiv.org/abs/1709.06901

PDF

https://arxiv.org/pdf/1709.06901.pdf

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