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Description

Programmatic Theme: Data Science

Disease named entity recognition (NER) is a critical task for most biomedical natural language processing (NLP) applications. For example, extracting diseases from clinical trial text can be helpful for patient profiling and other downstream applications such as matching clinical trials to eligible patients. Similarly, disease annotation in biomedical articles can help information search engines to accurately index them such that clinicians can easily find relevant articles to enhance their knowledge. In this paper, we propose a domain knowledge-enhanced long short-term memory network-conditional random field (LSTM-CRF) model for disease named entity recognition, which also augments a character-level convolutional neural network (CNN) and a character-level LSTM network for input embedding. Experimental results on a scientific article dataset show the effectiveness of our proposed models compared to state-of-the-art methods in disease recognition.

Learning Objective: Disease named entity recognition (NER) is a critical task for most biomedical natural language processing (NLP) applications. In this paper, our main objective is to achieve better accuracy scores compared to the current state-of-the-art methods in disease named entity recogntion. We proposed a domain knowledge-enhanced long short-term memory network-conditional random field (LSTM-CRF) model for this purpose.

Authors:

Yuan Ling, Philips Research North America
Sadid A. Hasan (Presenter)
Philips Research North America

Oladimeji Farri, Philips Research North America
Zheng Chen, Philips Research North America
Rob van Ommering, Philips Research North America
Charles Yee, Philips Research North America
Nevenka Dimitrova, Philips Research North America

Presentation Materials:

Keywords, Themes & Types