A Domain Knowledge - Enhanced Deep Learning Model For Disease Named Entity Recognition

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 talk, I will discuss about our recently proposed 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 demonstrate that our proposed model achieves new state-of-the-art results in disease named entity recognition on a scientific article dataset.ge caption generation.

Sadid Hasan, Senior Scientist at Philips Research

Sadid Hasan is a Senior Scientist at the Artificial Intelligence Lab in Philips Research North America, Cambridge, Massachusetts. His recent work involves solving problems related to clinical question answering, paraphrase generation, and medical image caption generation using Deep Learning. Before joining Philips, he was a Post-Doctoral Fellow at the Department of Mathematics and Computer Science, University of Lethbridge, Canada, from where he also obtained his PhD. in Computer Science with a focus in Computational Linguistics, Natural Language Processing (NLP), and Machine Learning in 2013.

Cookies help us deliver our services. By using our services, you agree to our use of cookies. Learn more