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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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Autori principali: 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
Natura: Artigo
Lingua:Inglês
Pubblicazione: Elsevier 2026-01-01
Serie:Journal of Allergy and Clinical Immunology: Global
Soggetti:
Hypereosinophilic syndrome
machine learning
prediction model
prevalence
Accesso online:http://www.sciencedirect.com/science/article/pii/S2772829325001894
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