Clinical Concept Extraction: a Methodology Review

TL;DR

This review systematically analyzes methods and tools for clinical concept extraction from electronic health records, highlighting development processes and considerations for improving NLP applications in healthcare.

Contribution

It provides a comprehensive methodology review of clinical concept extraction, cataloging development processes, available methods, tools, and specific considerations.

Findings

Reviewed 6,686 publications, selected 228 for detailed analysis.

Discussed various methods used in clinical concept extraction.

Highlighted key considerations for developing clinical NLP applications.

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.