Deep learning in clinical natural language processing: a methodical review

The University of Texas Health Science Center at Houston

PubMed
Indexed incrossrefpubmed

Abstract

Objective

This article methodically reviews the literature on deep learning (DL) for natural language processing (NLP) in the clinical domain, providing quantitative analysis to answer 3 research questions concerning methods, scope, and context of current research.

Materials And Methods

We searched MEDLINE, EMBASE, Scopus, the Association for Computing Machinery Digital Library, and the Association for Computational Linguistics Anthology for articles using DL-based approaches to NLP problems in electronic health records. After screening 1,737 articles, we collected data on 25 variables across 212 papers.

Citation impact

482
total citations
FWCI
29.89
Percentile
100%
References
90
Citations per year

Authors

12

Topics & keywords

Keywords
  • Artificial intelligence
  • Natural language processing
  • Computer science
  • Deep learning
  • Context (archaeology)
  • Information extraction
  • Named-entity recognition
  • Word2vec
UN Sustainable Development Goals
  • Quality Education
No related works found for this paper.

Funding