A survey of word embeddings for clinical text

[Display omitted] •We survey methods of representing clinical text using neural networks.•We provide a “how-to” guide for training these representations on clinical text.•We describe word models, corpora, evaluation methods, and applications. Representing words as numerical vectors based on the cont...

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Bibliographic Details
Published in:Journal of biomedical informatics 2019-01, Vol.100, p.100057-100057, Article 100057
Main Authors: Khattak, Faiza Khan, Jeblee, Serena, Pou-Prom, Chloé, Abdalla, Mohamed, Meaney, Christopher, Rudzicz, Frank
Format: Article
Language:English
Subjects:
Clinical data
Natural language processing
Word embeddings
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Summary:[Display omitted] •We survey methods of representing clinical text using neural networks.•We provide a “how-to” guide for training these representations on clinical text.•We describe word models, corpora, evaluation methods, and applications. Representing words as numerical vectors based on the contexts in which they appear has become the de facto method of analyzing text with machine learning. In this paper, we provide a guide for training these representations on clinical text data, using a survey of relevant research. Specifically, we discuss different types of word representations, clinical text corpora, available pre-trained clinical word vector embeddings, intrinsic and extrinsic evaluation, applications, and limitations of these approaches. This work can be used as a blueprint for clinicians and healthcare workers who may want to incorporate clinical text features in their own models and applications.
ISSN:1532-0464
1532-0480
DOI:10.1016/j.yjbinx.2019.100057