Bidirectional LSTM-CRF for Clinical Concept Extraction

Raghavendra Chalapathy, Ehsan Zare Borzeshi, Massimo Piccardi


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.
Anthology ID:
W16-4202
Volume:
Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP)
Month:
December
Year:
2016
Address:
Osaka, Japan
Venues:
ClinicalNLP | WS
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
7–12
Language:
URL:
https://www.aclweb.org/anthology/W16-4202
DOI:
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
http://aclanthology.lst.uni-saarland.de/W16-4202.pdf