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15 March 2019 Context-based bidirectional-LSTM model for sequence labeling in clinical reports
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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
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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);

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