Abstract
BackgroundWith 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. ObjectivesIn this literature review, we present a review of recent published research on clinical information extraction (IE) applications. MethodsA 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. ResultsA 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. ConclusionsClinical 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.
Highlights
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
Clinical information extraction (IE) has been used for a wide range of applications, there is a considerable gap between clinical studies using EHR data and studies using clinical IE
Since developing clinical natural language processing (NLP) talent is difficult in large part due to the limited availability of clinical data needed, we provided analysis of data sources used in clinical IE research and the accessibility of these data sources
Summary
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 to facilitate the use of EHR data for clinical decision support, quality improvement, or clinical and translation research is the information extraction (IE) task, which automatically extracts and encodes clinical information from text. IE is commonly recognized as a specialized area in empirical natural language processing (NLP) and refers to the automatic extraction of concepts, entities, and events, as well as their relations and associated attributes from free text [5,6,7]. 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
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