De-identification of medical records using conditional random fields and long short-term memory networks.
J Biomed Inform
; 75S: S43-S53, 2017 Nov.
Article
em En
| MEDLINE
| ID: mdl-29032162
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 0.8986, which was higher than that of the CRF-based system.
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Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Prontuários Médicos
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Anonimização de Dados
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Memória de Curto Prazo
Idioma:
En
Ano de publicação:
2017
Tipo de documento:
Article