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 - Publisher Copyright:
© 2020 Elsevier Inc.
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
SN - 1532-0464
VL - 109
JO - Journal of Biomedical Informatics
JF - Journal of Biomedical Informatics
M1 - 103526
ER -