Journal Articles

Publication Date



American Medical Informatics Association Annual Symposium


We propose a frame-based natural language processing (NLP) method that extracts cancer-related information from clinical narratives. We focus on three frames: cancer diagnosis, cancer therapeutic procedure, and tumor description. We utilize a deep learning-based approach, bidirectional Long Short-term Memory (LSTM) Conditional Random Field (CRF), which uses both character and word embeddings. The system consists of two constituent sequence classifiers: a frame identification (lexical unit) classifier and a frame element classifier. The classifier achieves an F


Datasets as Topic, Deep Learning, Electronic Health Records, Heuristics, Humans, Information Storage and Retrieval, Natural Language Processing, Neoplasms, Neural Networks, Computer, Semantics



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