Context-based bidirectional-LSTM model for sequence labeling in clinical reports

Henghui Zhu; Ioannis Ch. Paschalidis; Amir M. Tahmasebi

doi:10.1117/12.2512103

15 March 2019 Context-based bidirectional-LSTM model for sequence labeling in clinical reports

Henghui Zhu, Ioannis Ch. Paschalidis, Amir M. Tahmasebi

Proceedings Volume 10954, Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications; 109540J (2019) https://doi.org/10.1117/12.2512103
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Recurrent Neural Network (RNN) models have been widely used for sequence labeling applications in different domains. This paper presents an RNN-based sequence labeling approach with the ability to learn long-term labeling dependencies. The proposed model has been successfully used for a Named Entity Recognition challenge in healthcare: anatomical phrase labeling in radiology reports. The model was trained on labeled data from a radiology report corpus and tested on two independent datasets. The proposed model achieved promising performance in comparison with other state-of-the-art context-driven sequence labeling approaches.

Conference Presentation

Citation Download Citation

Henghui Zhu, Ioannis Ch. Paschalidis, and Amir M. Tahmasebi "Context-based bidirectional-LSTM model for sequence labeling in clinical reports", Proc. SPIE 10954, Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications, 109540J (15 March 2019); https://doi.org/10.1117/12.2512103

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