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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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Artificial intelligence approaches using natural language processing to advance EHR-based clinical research.

Young Juhn1, Hongfang Liu2

  • 1Precision 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.

The Journal of Allergy and Clinical Immunology
|December 30, 2019
PubMed
Summary
This summary is machine-generated.

Electronic health records generate big data for research. Natural language processing unlocks clinical narratives, enabling automated patient identification for allergy, asthma, and immunology studies.

Keywords:
EHRsalgorithmsallergyartificial intelligenceasthmadata miningimmunologyinformaticsmachine learningnatural language processing

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Area of Science:

  • Health Informatics
  • Clinical Research
  • Artificial Intelligence

Background:

  • Electronic health record (EHR) systems generate vast real-world data for clinical research.
  • Clinical narratives within EHRs contain valuable information but are often unstructured.
  • Natural Language Processing (NLP) offers a method to extract data from these narratives.

Purpose of the Study:

  • To review the current literature on the secondary use of EHR data in allergy, asthma, and immunology research.
  • To highlight the potential and challenges of using NLP for information extraction from EHRs.
  • To discuss the implications of NLP in improving clinical research methodologies.

Main Methods:

  • Literature review of studies utilizing EHR data for clinical research.
  • Focus on applications of Natural Language Processing (NLP) techniques.
  • Analysis of NLP's role in phenotype definition and patient identification.

Main Results:

  • NLP enables automated chart review and identification of patients with specific clinical characteristics.
  • NLP can reduce methodological heterogeneity in phenotype definition.
  • Potential to better understand biological heterogeneity in allergy, asthma, and immunology.

Conclusions:

  • NLP is a powerful tool for leveraging EHR data in specialized research areas.
  • Addressing challenges in NLP implementation is crucial for its successful adoption.
  • NLP has significant implications for advancing allergy, asthma, and immunology research through improved data utilization.