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arXiv · 1611.08373

Bidirectional LSTM-CRF for Clinical Concept Extraction

Raghavendra Chalapathy·Ehsan Zare Borzeshi·Massimo Piccardi
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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.

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BibTeXRIS

Raghavendra Chalapathy, Ehsan Zare Borzeshi, Massimo Piccardi. 2016-11-25. Bidirectional LSTM-CRF for Clinical Concept Extraction. https://arxiv.org/abs/1611.08373

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