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., ...More

The Journal of allergy and clinical immunology, vol.157, no.2, pp.470-485, 2026 (SCI-Expanded, Scopus) identifier identifier

  • Publication Type: Article / Article
  • Volume: 157 Issue: 2
  • Publication Date: 2026
  • Doi Number: 10.1016/j.jaci.2025.10.022
  • Journal Name: The Journal of allergy and clinical immunology
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, EMBASE, MEDLINE, Nature Index
  • Page Numbers: pp.470-485
  • Keywords: 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 University Affiliated: Yes