@InProceedings{chalapathy-zareborzeshi-piccardi:2016:ClinicalNLP,
  author    = {Chalapathy, Raghavendra  and  Zare Borzeshi, Ehsan  and  Piccardi, Massimo},
  title     = {Bidirectional LSTM-CRF for Clinical Concept Extraction},
  booktitle = {Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {7--12},
  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.},
  url       = {http://aclweb.org/anthology/W16-4202}
}