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A survey of word embeddings for clinical text - 2019

A Survey Of Word Embeddings For Clinical Text

Research Area:  Machine Learning

Abstract:

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.

Keywords:  

Author(s) Name:  Faiza Khan Khattaka,Serena Jeblee, ChloéPou-Prom,Mohamed Abdalla,Christopher Meaney,Frank Rudzicz

Journal name:  Journal of Biomedical Informatics

Conferrence name:  

Publisher name:  Elsevier B.V.

DOI:  https://doi.org/10.1016/j.yjbinx.2019.100057

Volume Information:  Volume 100, Supplement, 2019, 100057