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...

תיאור מלא

מידע ביבליוגרפי

Principais autores:	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
פורמט:	Artigo
שפה:	Inglês
יצא לאור:	Elsevier 2026-01-01
סדרה:	Journal of Allergy and Clinical Immunology: Global
נושאים:	Hypereosinophilic syndrome machine learning prediction model prevalence
גישה מקוונת:	http://www.sciencedirect.com/science/article/pii/S2772829325001894
תגים:	הוספת תג אין תגיות, היה/י הראשונ/ה לתייג את הרשומה!