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.
Dr. Sadid Hasan is a Senior Scientist in the Artificial Intelligence Group at Philips Research, Cambridge, Massachusetts. His recent work involves solving problems related to clinical information extraction, paraphrase generation, natural language inference, and clinical question answering using Deep Learning. Sadid has over 60 peer-reviewed publications in the top NLP/Machine Learning venues, where he also regularly serves as a program committee member/area chair including ACL, IJCAI, EMNLP, NeurIPS, ICML, COLING, NAACL, AMIA, MLHC, MEDINFO, ICLR, ClinicalNLP, TKDE, JAIR etc.