Reviews and feature articleArtificial intelligence approaches using natural language processing to advance EHR-based clinical research
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Systematic review for NLP-based research in allergy, asthma, and immunology
To assess the current state of EHR-based research using NLP in the field of asthma, allergy, and immunology, a comprehensive literature search of several databases from January 1, 2000, to August 13, 2019, English language, was conducted (see Table E1 in this article’s Online Repository at www.jacionline.org for the detailed method for our systematic literature search and the summary of results of each study). Only 21 articles were included in this systematic review by excluding abstracts,
Introduction for NLP
We discuss a brief overview of NLP to introduce the conceptual understanding of NLP for clinicians but do not intend to cover the topic of NLP for the purpose of performing NLP-based research. We refer readers to a few review articles that discuss NLP in depth.38, 39, 40 NLP is a field in computer science, AI, and computational linguistics that enables interactions between computers and human languages and bridges the gap between clinical human language and computational systems. NLP, ML, and
Implications of NLP for EHR based on clinical research and care
Fig 2 illustrates modular components of EHRs (top row) that encompass specific clinical information in both structured (eg, laboratory data) and nonstructured (eg, clinical notes) formats (bottom row), and the multimodal capability of NLP can extract and process this information, and classify patients, enabling important clinical tasks (right column) such as quality report and clinical decision support. Thus, NLP might be an important method to make EHRs a helpful data source for addressing the
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The work was supported by grants from the National Institute of Health (grant no. R01 HL126667) and the R21 grant (grant no. R21AI116839-01).
Disclosure of potential conflict of interest: The authors declare that they have no relevant conflicts of interest.