Natural Language Processing in Electronic Health Records in relation to healthcare decision-making: A systematic review.

Journal: Computers in biology and medicine
Published Date: Feb 10, 2023

Abstract

BACKGROUND: Natural Language Processing (NLP) is widely used to extract clinical insights from Electronic Health Records (EHRs). However, the lack of annotated data, automated tools, and other challenges hinder the full utilisation of NLP for EHRs. Various Machine Learning (ML), Deep Learning (DL) and NLP techniques are studied and compared to understand the limitations and opportunities in this space comprehensively.

Authors

  • Elias Hossain
    School of Engineering & Physical Sciences, North South University, Dhaka 1229, Bangladesh. Electronic address: elias.hossain191@gmail.com.
  • Rajib Rana
    School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield Central QLD 4300, Australia.
  • Niall Higgins
    Metro North Hospital and Health Service, Royal Brisbane and Women's Hospital, Herston 4029, Australia.
  • Jeffrey Soar
    School of Management and Enterprise, University of Southern Queensland, Springfield, Qld 4300, Australia. Electronic address: jeffrey.soar@usq.edu.au.
  • Prabal Datta Barua
    Cogninet Australia, Sydney, NSW 2010 Australia.
  • Anthony R Pisani
    Department of Psychiatry, University of Rochester Medical Center, Rochester, NY, 14642, USA.
  • Kathryn Turner
    School of Nursing, Queensland University of Technology, Kelvin Grove, Brisbane, QLD 4000, Australia.

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