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Cited by 910Biomedical Text Mining and OntologiesMachine Learning in HealthcareElectronic Health Records Systems

Clinical information extraction applications: A literature review

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Wang, Yanshan, Wang, Liwei, Rastegar-Mojarad, Majid, Moon, Sungrim, Shen, Feichen, Afzal, Naveed, Liu, Sijia, Zeng, Yuqun +3 more·2018·Journal of Biomedical Informatics
DOI 10.1016/j.jbi.2017.11.011PMC PMC5771858
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Abstract

BACKGROUND: With the rapid adoption of electronic health records (EHRs), it is desirable to harvest information and knowledge from EHRs to support automated systems at the point of care and to enable secondary use of EHRs for clinical and translational research. One critical component used to facilitate the secondary use of EHR data is the information extraction (IE) task, which automatically extracts and encodes clinical information from text. OBJECTIVES: In this literature review, we present a review of recent published research on clinical information extraction (IE) applications. METHODS: A literature search was conducted for articles published from January 2009 to September 2016 based on Ovid MEDLINE In-Process & Other Non-Indexed Citations, Ovid MEDLINE, Ovid EMBASE, Scopus, Web of Science, and ACM Digital Library. RESULTS: A total of 1917 publications were identified for title and abstract screening. Of these publications, 263 articles were selected and discussed in this review in terms of publication venues and data sources, clinical IE tools, methods, and applications in the areas of disease- and drug-related studies, and clinical workflow optimizations. CONCLUSIONS: Clinical IE has been used for a wide range of applications, however, there is a considerable gap between clinical studies using EHR data and studies using clinical IE. This study enabled us to gain a more concrete understanding of the gap and to provide potential solutions to bridge this gap.

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Cited by

Sorted by citations

Authors
Wang, Y., Wang, L., Rastegar-Mojarad, M., Moon, S., Shen, F., Afzal, N., Liu, S., Zeng, Y., Mehrabi, S., Sohn, S., & Liu, H.
Affiliations
Mayo Clinic (US)
Corresponding author
Liu, Hongfang
ORCID
Venue
Journal of Biomedical Informatics
Publisher
Elsevier BV
Published
2018-01
Volume
77
Pages
34-49
ISSN
1532-0464 (ISSN-L)
DOI
10.1016/j.jbi.2017.11.011
PMC
PMC5771858
OpenAlex
W2768488789
Cited by
910
References
5
Citations by year
2026: 128 · 2025: 132 · 2024: 111 · 2023: 104 · 2022: 98 · 2021: 100 · 2020: 101 · 2019: 101 · 2018: 33 · 2017: 1
FWCI
6.04
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Biomedical Informatics (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • National Institutes of Health (U01TR002062)
  • National Institute of General Medical Sciences (R01GM102282)
  • National Institute of Biomedical Imaging and Bioengineering (R01EB19403, R01EB019403)
  • U.S. National Library of Medicine (R01LM011934)
  • National Center for Advancing Translational Sciences
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
Keywords
clinical information extraction, electronic health records, clinical text mining, clinical workflow optimization, clinical research data extraction
MeSH terms
Humans, Medical Informatics, Natural Language Processing, Research Design, Information Storage and Retrieval, Electronic Health Records, Meaningful Use

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

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What counts as a claim ›How we measure confidence ›How we check this ›

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