A Frame-Based NLP System for Cancer-Related Information Extraction.

Authors

Yuqi Si, University of Texas Health Science Center at Houston, School of Health Information Sciences, Houston TX, USA
Kirk Roberts

Publication Date

1-1-2018

Journal

American Medical Informatics Association Annual Symposium

Abstract

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

Keywords

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