@inproceedings{chalapathy-etal-2016-bidirectional,
title = "Bidirectional {LSTM}-{CRF} for Clinical Concept Extraction",
author = "Chalapathy, Raghavendra and
Zare Borzeshi, Ehsan and
Piccardi, Massimo",
editor = "Rumshisky, Anna and
Roberts, Kirk and
Bethard, Steven and
Naumann, Tristan",
booktitle = "Proceedings of the Clinical Natural Language Processing Workshop ({C}linical{NLP})",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/W16-4202",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Bidirectional LSTM-CRF for Clinical Concept Extraction
%A Chalapathy, Raghavendra
%A Zare Borzeshi, Ehsan
%A Piccardi, Massimo
%Y Rumshisky, Anna
%Y Roberts, Kirk
%Y Bethard, Steven
%Y Naumann, Tristan
%S Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F chalapathy-etal-2016-bidirectional
%X 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.
%U https://aclanthology.org/W16-4202
%P 7-12
Markdown (Informal)
[Bidirectional LSTM-CRF for Clinical Concept Extraction](https://aclanthology.org/W16-4202) (Chalapathy et al., ClinicalNLP 2016)
ACL
- Raghavendra Chalapathy, Ehsan Zare Borzeshi, and Massimo Piccardi. 2016. Bidirectional LSTM-CRF for Clinical Concept Extraction. In Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP), pages 7–12, Osaka, Japan. The COLING 2016 Organizing Committee.