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The Journal of allergy and clinical immunology
02, 2020;145 (2): 463-469.

Artificial intelligence approaches using natural language processing to advance EHR-based clinical research.

Young Juhn, Hongfang Liu
Author Information
  1. Young Juhn: Precision Population Science Lab, Division of Community Pediatric and Adolescent Medicine, Department of Pediatric and Adolescent Medicine, Rochester, Minn; Division of Allergy, Department of Medicine, Mayo Clinic, Rochester, Minn. Electronic address: juhn.young@mayo.edu.
  2. Hongfang Liu: Division of Digital Health, Department of Health Sciences Research, Mayo Clinic, Rochester, Minn.
PMID: 31883846 DOI: 10.1016/j.jaci.2019.12.897

Abstract

The wide adoption of electronic health record systems in health care generates big real-world data that open new venues to conduct clinical research. As a large amount of valuable clinical information is locked in clinical narratives, natural language processing techniques as an artificial intelligence approach have been leveraged to extract information from clinical narratives in electronic health records. This capability of natural language processing potentially enables automated chart review for identifying patients with distinctive clinical characteristics in clinical care and reduces methodological heterogeneity in defining phenotype, obscuring biological heterogeneity in research concerning allergy, asthma, and immunology. This brief review discusses the current literature on the secondary use of electronic health record data for clinical research concerning allergy, asthma, and immunology and highlights the potential, challenges, and implications of natural language processing techniques.

Keywords

EHRs algorithms allergy artificial intelligence asthma data mining immunology informatics machine learning natural language processing

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Grants

  1. R01 HL126667/NHLBI NIH HHS
  2. R21 AI116839/NIAID NIH HHS

MeSH Term

Allergy and Immunology
Electronic Health Records
Humans
Natural Language Processing
Research Design
Journal Article Research Support, N.I.H., Extramural Review
Article has an altmetric score of 27

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