Prediction model to identify patients with hypereosinophilic syndrome using real-world data
A machine learning prediction model identified a substantial number of patients with predicted HES, suggesting that the actual prevalence of HES may be significantly underestimated.
Authors
- Paneez Khoury (ORCID: https://orcid.org/0000-0003-1800-0079)
- Elizabeth Judson
- Erin Cook (ORCID: https://orcid.org/0000-0001-9737-9130)
- Princess U. Ogbogu
- Donna Carstens
- Yen Chung (ORCID: https://orcid.org/0000-0003-1587-4774)
- Fan Mu (ORCID: https://orcid.org/0000-0003-0252-0387)
- Travis Wang (ORCID: https://orcid.org/0000-0002-1175-1884)
- Jingyi Chen (ORCID: https://orcid.org/0009-0007-0141-2241)
- Zhuo Chen (ORCID: https://orcid.org/0009-0009-6026-2006)
- Mu Cheng
Institutions
- AstraZeneca (United Kingdom) (GB)
- AstraZeneca (Singapore) (SG)
- National Institute of Allergy and Infectious Diseases (US)
- AstraZeneca (United States) (US)
- Rainbow Babies & Children's Hospital (US)
- University School (US)
- Case Western Reserve University (US)
- Analysis Group (United States) (US)
Publication Details
- Journal
- Journal of Allergy and Clinical Immunology Global
- Published
- 2025-10-25
- DOI
- https://doi.org/10.1016/j.jacig.2025.100588
- Citations
- 1
- Primary Topic
- Eosinophilic Disorders and Syndromes
- Type
- article
- Field-Weighted Citation Impact
- 1.67