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Proceedings Paper

Context-based bidirectional-LSTM model for sequence labeling in clinical reports
Author(s): Henghui Zhu; Ioannis Ch. Paschalidis; Amir M. Tahmasebi
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Paper 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.

Paper Details

Date Published: 15 March 2019
PDF: 6 pages
Proc. SPIE 10954, Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications, 109540J (15 March 2019); doi: 10.1117/12.2512103
Show Author Affiliations
Henghui Zhu, Boston Univ. (United States)
Ioannis Ch. Paschalidis, Boston Univ. (United States)
Amir M. Tahmasebi, Philips Research North America (United States)


Published in SPIE Proceedings Vol. 10954:
Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications
Po-Hao Chen; Peter R. Bak, Editor(s)

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