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Artificial Intelligence and Machine Learning for Inborn Errors of Immunity: Current State and Future Promise.
Martinson, Alexandra K; Chin, Aaron T; Butte, Manish J; Rider, Nicholas L.
Afiliación
  • Martinson AK; Department of Pediatrics, Children's National Hospital, Washington, DC.
  • Chin AT; Department of Pediatrics, Division of Immunology, Allergy and Rheumatology, University of California, Los Angeles, Los Angeles, Calif.
  • Butte MJ; Department of Pediatrics, Division of Immunology, Allergy and Rheumatology, University of California, Los Angeles, Los Angeles, Calif.
  • Rider NL; Department of Health Systems & Implementation Science, Virginia Tech Carilion School of Medicine, Roanoke, Va; Department of Medicine, Division of Allergy-Immunology, Carilion Clinic, Roanoke, Va. Electronic address: nick70@vt.edu.
J Allergy Clin Immunol Pract ; 12(10): 2695-2704, 2024 Oct.
Article en En | MEDLINE | ID: mdl-39127104
ABSTRACT
Artificial intelligence (AI) and machine learning (ML) research within medicine has exponentially increased over the last decade, with studies showcasing the potential of AI/ML algorithms to improve clinical practice and outcomes. Ongoing research and efforts to develop AI-based models have expanded to aid in the identification of inborn errors of immunity (IEI). The use of larger electronic health record data sets, coupled with advances in phenotyping precision and enhancements in ML techniques, has the potential to significantly improve the early recognition of IEI, thereby increasing access to equitable care. In this review, we provide a comprehensive examination of AI/ML for IEI, covering the spectrum from data preprocessing for AI/ML analysis to current applications within immunology, and address the challenges associated with implementing clinical decision support systems to refine the diagnosis and management of IEI.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Aprendizaje Automático Límite: Humans Idioma: En Revista: J Allergy Clin Immunol Pract Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Aprendizaje Automático Límite: Humans Idioma: En Revista: J Allergy Clin Immunol Pract Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos