Self-Supervised Contextual Language Representation of Radiology Reports to Improve the Identification of Communication Urgency.
AMIA Jt Summits Transl Sci Proc
; 2020: 413-421, 2020.
Article
em En
| MEDLINE
| ID: mdl-32477662
ABSTRACT
Machine learning methods have recently achieved high-performance in biomedical text analysis. However, a major bottleneck in the widespread application of these methods is obtaining the required large amounts of annotated training data, which is resource intensive and time consuming. Recent progress in self-supervised learning has shown promise in leveraging large text corpora without explicit annotations. In this work, we built a self-supervised contextual language representation model using BERT, a deep bidirectional transformer architecture, to identify radiology reports requiring prompt communication to the referring physicians. We pre-trained the BERT model on a large unlabeled corpus of radiology reports and used the resulting contextual representations in a final text classifier for communication urgency. Our model achieved a precision of 97.0%, recall of 93.3%, and F-measure of 95.1% on an independent test set in identifying radiology reports for prompt communication, and significantly outperformed the previous state-of-the-art model based on word2vec representations.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Diagnostic_studies
Idioma:
En
Revista:
AMIA Jt Summits Transl Sci Proc
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
Estados Unidos