TY - JOUR
T1 - Clinical concept extraction
T2 - A methodology review
AU - Fu, Sunyang
AU - Chen, David
AU - He, Huan
AU - Liu, Sijia
AU - Moon, Sungrim
AU - Peterson, Kevin J.
AU - Shen, Feichen
AU - Wang, Liwei
AU - Wang, Yanshan
AU - Wen, Andrew
AU - Zhao, Yiqing
AU - Sohn, Sunghwan
AU - Liu, Hongfang
N1 - Funding Information:
We gratefully acknowledge Larry J. Prokop for implementing search strategies, and Katelyn Cordie and Luke Carlson for editorial support. This work was made possible by National Institute of Health (NIH) grant number 1U01TR002062-01 and R21AI142702 .
PY - 2020/9
Y1 - 2020/9
N2 - 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.
AB - 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.
KW - Concept extraction
KW - Deep learning
KW - Electronic health records
KW - Information extraction
KW - Machine learning
KW - Natural language processing
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U2 - 10.1016/j.jbi.2020.103526
DO - 10.1016/j.jbi.2020.103526
M3 - Review article
C2 - 32768446
AN - SCOPUS:85090042452
VL - 109
JO - Journal of Biomedical Informatics
JF - Journal of Biomedical Informatics
SN - 1532-0464
M1 - 103526
ER -