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Title
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Unsupervised concept extraction from clinical text through semantic composition
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Author
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Abstract
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| Concept extraction is an important step in clinical natural language processing. Once extracted, the use of concepts can improve the accuracy and generalization of downstream systems. We present a new unsupervised system for the extraction of concepts from clinical text. The system creates representations of concepts from the Unified Medical Language System (UMLS®) by combining natural language descriptions of concepts with word representations, and composing these into higher-order concept vectors. These concept vectors are then used to assign labels to candidate phrases which are extracted using a syntactic chunker. Our approach scores an exact F-score of.32 and an inexact F-score of.45 on the well-known I2b2-2010 challenge corpus, outperforming the only other unsupervised concept extraction method. As our approach relies only on word representations and a chunker, it is completely unsupervised. As such, it can be applied to languages and corpora for which we do not have prior annotations. All our code is open-source and can be found at www.github.com/clips/conch. |
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Language
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English
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Source (journal)
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Journal of biomedical informatics. - New York
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Publication
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New York
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2019
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ISSN
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1532-0464
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DOI
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10.1016/J.JBI.2019.103120
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Volume/pages
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91
(2019)
, p. 1-11
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ISI
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000525688200013
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Full text (Publisher's DOI)
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