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

[Display omitted] •The described LSTM model attains F1 measure of 0.8986 in CEGS N-GRID 2016 Shared Task.•The LSTM-based model attains higher F1 measure than the CRF-based model.•Accurate sentence detection and tokenization can significantly improve the performance. 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 0.8986, which was higher than that of the CRF-based system.