Clinical concept extraction: A methodology review

Sunyang Fu, David Chen, Huan He, Sijia Liu, Sungrim Moon, Kevin J. Peterson, Feichen Shen, Liwei Wang, Yanshan Wang, Andrew Wen, Yiqing Zhao, Sunghwan Sohn, Hongfang Liu

Research output: Contribution to journalReview articlepeer-review

1 Scopus citations

Abstract

Background: Concept extraction, a subdomain of natural language processing (NLP) with a focus on extracting concepts of interest, has been adopted to computationally extract clinical information from text for a wide range of applications ranging from clinical decision support to care quality improvement. Objectives: In this literature review, we provide a methodology review of clinical concept extraction, aiming to catalog development processes, available methods and tools, and specific considerations when developing clinical concept extraction applications. Methods: Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a literature search was conducted for retrieving EHR-based information extraction articles written in English and published from January 2009 through June 2019 from Ovid MEDLINE In-Process & Other Non-Indexed Citations, Ovid MEDLINE, Ovid EMBASE, Scopus, Web of Science, and the ACM Digital Library. Results: A total of 6,686 publications were retrieved. After title and abstract screening, 228 publications were selected. The methods used for developing clinical concept extraction applications were discussed in this review.

Original languageEnglish (US)
Article number103526
JournalJournal of Biomedical Informatics
Volume109
DOIs
StatePublished - Sep 2020

Keywords

  • Concept extraction
  • Deep learning
  • Electronic health records
  • Information extraction
  • Machine learning
  • Natural language processing

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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