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Prediction model to identify patients with hypereosinophilic syndrome using real-world data

Background: Hypereosinophilic syndrome (HES) is challenging to diagnose and identify in real-world data. Objective: We sought to develop a machine learning model to predict HES diagnosis in secondary data and estimate HES prevalence among individuals with elevated blood eosinophil count (BEC). Metho...

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Autors principals: Paneez Khoury, MD, Yen Chung, PharmD, Donna Carstens, MD, Erin E. Cook, ScD, Fan Mu, ScD, Mu Cheng, MPH, Elizabeth Judson, MPH, Jingyi Chen, MSc, Travis Wang, MSc, Zhuo Chen, MPH, Princess U. Ogbogu, MD
Format: Artigo
Idioma:Inglês
Publicat: Elsevier 2026-01-01
Col·lecció:Journal of Allergy and Clinical Immunology: Global
Matèries:
Hypereosinophilic syndrome
machine learning
prediction model
prevalence
Accés en línia:http://www.sciencedirect.com/science/article/pii/S2772829325001894
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