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

2017/09/20 by Zhipeng Jiang, Jiang, Zhipeng, Chao Zhao +7 · 1 citation
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning in Healthcare #Natural Language Processing Techniques #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1709.06901

openalex publication_date 2017/09/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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-F₁ measure of 89.86%, which was higher than that of the CRF-based system.

Citations

• Long Short-Term Memory

Cited by

• Towards Automatic Evaluation and Selection of PHI De-identification Models via Multi-Agent Collaboration

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