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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| Published in: | Journal of biomedical informatics Vol. 100; p. 100057 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Inc
01.01.2019
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| Subjects: | |
| ISSN: | 1532-0464, 1532-0480, 1532-0480 |
| Online Access: | Get full text |
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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. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 ObjectType-Review-3 content type line 23 |
| ISSN: | 1532-0464 1532-0480 1532-0480 |
| DOI: | 10.1016/j.yjbinx.2019.100057 |