Bidirectional LSTM-CRF for Clinical Concept Extraction
Abstract
Automated extraction of concepts from patient clinical records is an essential facilitator of clinical research. For this reason, the 2010 i2b2/VA Natural Language Processing Challenges for Clinical Records introduced a concept extraction task aimed at identifying and classifying concepts into predefined categories (i.e., treatments, tests and problems). State-of-the-art concept extraction approaches heavily rely on handcrafted features and domain-specific resources which are hard to collect and define. For this reason, this paper proposes an alternative, streamlined approach: a recurrent neural network (the bidirectional LSTM with CRF decoding) initialized with general-purpose, off-the-shelf word embeddings. The experimental results achieved on the 2010 i2b2/VA reference corpora using the proposed framework outperform all recent methods and ranks closely to the best submission from the original 2010 i2b2/VA challenge.
Cite
@article{arxiv.1611.08373,
title = {Bidirectional LSTM-CRF for Clinical Concept Extraction},
author = {Raghavendra Chalapathy and Ehsan Zare Borzeshi and Massimo Piccardi},
journal= {arXiv preprint arXiv:1611.08373},
year = {2016}
}
Comments
This paper "Bidirectional LSTM-CRF for Clinical Concept Extraction" is accepted for short paper presentation at Clinical Natural Language Processing Workshop at COLING 2016 Osaka, Japan. December 11, 2016