Machine learning–assisted diagnosis classification of primary immune dysregulation using IDDA2.1 phenotype profiling
Random forest feature importance analysis can inform targeted clinical screening for key disease manifestations. The top 3 prediction approach demonstrates diagnostic potential, but improved accuracy will require larger, globally shared datasets. Small sample sizes for rare diseases highlight the necessity of broader collaboration to enhance AI-assisted clinical decision-making in the future.
Authors
- Sai Veeranki (ORCID: https://orcid.org/0000-0003-2009-6428)
- Markus G. Seidel (ORCID: https://orcid.org/0000-0003-0981-8661)
- Benedikt N. Seidel
- Stephan Rusch (ORCID: https://orcid.org/0000-0002-3529-1384)
- Gerhard Kindle (ORCID: https://orcid.org/0000-0003-0894-5327)
- Malte Schwitzkowski
Institutions
- Medical University of Graz (AT)
- University Medical Center Freiburg (DE)
- Center for Cancer and Blood Disorders (US)
- Interdisciplinary Research Centre for Technology, Work and Culture (AT)
- Austrian Mobility Research (AT)
Publication Details
- Journal
- Journal of Allergy and Clinical Immunology
- Published
- 2025-11-05
- DOI
- https://doi.org/10.1016/j.jaci.2025.10.022
- Primary Topic
- Immunodeficiency and Autoimmune Disorders
- Type
- article
- Field-Weighted Citation Impact
- 0.00