Machine learning-assisted diagnosis classification of primary immune dysregulation using IDDA2.1 phenotype profiling.

Schwitzkowski M., Veeranki S. P. K., Seidel B. N., Kindle G., Rusch S., Kramer D., ...Daha Fazla

The Journal of allergy and clinical immunology, cilt.157, sa.2, ss.470-485, 2026 (SCI-Expanded, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 157 Sayı: 2
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1016/j.jaci.2025.10.022
  • Dergi Adı: The Journal of allergy and clinical immunology
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, EMBASE, MEDLINE, Nature Index
  • Sayfa Sayıları: ss.470-485
  • Anahtar Kelimeler: artificial intelligence (AI), immune deficiency and dysregulation activity (IDDA) score, Inborn error of immunity (IEI), interoperable patient data, phenotype-driven disease classification, primary immune disorder (PID), primary immune regulatory disorder (PIRD), primary immunodeficiency (PID), unsupervised and supervised machine learning (ML)
  • Ankara Üniversitesi Adresli: Evet