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J Biomed Inform. 2018 Jan;77:34-49. doi: 10.1016/j.jbi.2017.11.011. Epub 2017 Nov 21.

Clinical information extraction applications: A literature review.

Author information

1
Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States.
2
Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States. Electronic address: liu.hongfang@mayo.edu.

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.

KEYWORDS:

Application; Clinical notes; Electronic health records; Information extraction; Natural language processing

PMID:
29162496
PMCID:
PMC5771858
DOI:
10.1016/j.jbi.2017.11.011
[Indexed for MEDLINE]
Free PMC Article

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