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Deep learning in clinical natural language processing: a methodical review.

Stephen Wu1, Kirk Roberts1, Surabhi Datta1

  • 1School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.

Journal of the American Medical Informatics Association : JAMIA
|December 4, 2019
PubMed
Summary
This summary is machine-generated.

Deep learning for clinical natural language processing (NLP) is rapidly growing, with recurrent neural networks and word2vec embeddings being popular. This field shows increasing acceptance in the medical community.

Keywords:
deep learningelectronic health recordsmethodical, review, clinical textnatural language processing

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Area of Science:

  • Medical Informatics
  • Computational Linguistics
  • Artificial Intelligence

Background:

  • Deep learning (DL) is increasingly applied to natural language processing (NLP) tasks in the clinical domain.
  • Electronic health records (EHRs) present unique challenges and opportunities for NLP.
  • Understanding the landscape of DL in clinical NLP is crucial for future research and development.

Purpose of the Study:

  • To systematically review and quantitatively analyze the literature on deep learning for clinical NLP.
  • To answer research questions regarding the methods, scope, and context of current DL-based clinical NLP research.
  • To identify trends and popular approaches in this rapidly evolving field.

Main Methods:

  • A comprehensive literature search was conducted across major scientific databases (MEDLINE, EMBASE, Scopus, ACM DL, ACL Anthology).
  • Articles employing DL-based NLP approaches for EHR data were screened and analyzed.
  • Data on 25 variables were collected from 212 selected papers.

Main Results:

  • Publications on DL in clinical NLP more than doubled annually through 2018.
  • Recurrent neural networks (60.8%) and word2vec embeddings (74.1%) were the most prevalent methods.
  • Information extraction tasks, including text classification, named entity recognition, and relation extraction, dominated the research (89.2%).

Conclusions:

  • Deep learning is gaining traction as a baseline for NLP research in the medical community.
  • While common associations between methods and tasks were observed, nuances such as the scarcity of French clinical NLP were also identified.
  • Deep learning has not yet fully permeated clinical NLP but is experiencing rapid growth, with both popular and unique trends emerging.