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Machine learning in allergy research: A bibliometric review.

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Artificial intelligence and machine learning (AI/ML) can advance allergy research by analyzing complex data. These approaches improve patient care through better prevention, diagnosis, and management of allergic diseases.

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Area of Science:

  • Computational biology
  • Immunology
  • Data science

Background:

  • Big data analytics enable characterization of allergic disease subtypes.
  • Multi-dimensional data (genomics, epigenomics, etc.) aid causal analysis.
  • Understanding disease progression requires integrating diverse biological data.

Purpose of the Study:

  • To systematically review the role of machine learning (ML) in allergy research.
  • To assess the potential of AI/ML in advancing the understanding of allergic diseases.
  • To identify how AI/ML can enhance patient care strategies.

Main Methods:

  • Comprehensive systematic literature review.
  • Analysis of current research on AI/ML applications in allergy.
  • Triangulation of data from various sources and study types.

Main Results:

  • AI/ML approaches show significant potential in allergy research.
  • These methods can improve prevention, diagnosis, and management of allergic diseases.
  • No single analytical method is universally optimal.

Conclusions:

  • AI/ML holds promise for transforming allergy research and patient outcomes.
  • Cross-disciplinary collaboration and team science are crucial for effective application.
  • Context-specific interpretation requires input from clinicians and data analysts.