A validated artificial intelligence-based pipeline for population-wide primary immunodeficiency screening.

Journal: The Journal of allergy and clinical immunology
Published Date: Oct 13, 2022

Abstract

BACKGROUND: Identification of patients with underlying inborn errors of immunity and inherent susceptibility to infection remains challenging. The ensuing protracted diagnostic odyssey for such patients often results in greater morbidity and suboptimal outcomes, underscoring a need to develop systematic methods for improving diagnostic rates.

Authors

  • Nicholas L Rider
    Department of Health Systems & Implementation Science, Virginia Tech Carilion School of Medicine, Division of Allergy-Immunology Carilion Clinic, Roanoke, VA, United States.
  • Michael Coffey
    Department of Information Services, Texas Children's Hospital, Houston, Tex.
  • Ashok Kurian
    Department of Information Services, Texas Children's Hospital, Houston, Tex.
  • Jessica Quinn
    Jeffrey Modell Foundation, Columbia New York Presbyterian Hospital, New York, NY.
  • Jordan S Orange
    Division of Pediatrics, Morgan S. Stanley Children's Hospital, Columbia New York Presbyterian Hospital, New York, NY.
  • Vicki Modell
    Jeffrey Modell Foundation, Columbia New York Presbyterian Hospital, New York, NY.
  • Fred Modell
    Jeffrey Modell Foundation, Columbia New York Presbyterian Hospital, New York, NY.

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