Enhancing the Prediction of Inborn Errors of Immunity: Integrating Jeffrey Modell Foundation Criteria with Clinical Variables Using Machine Learning

<b>Background</b>: Inborn errors of immunity (IEIs) are a heterogeneous group of rare disorders caused by genetic defects in one or more components of the immune system. The Jeffrey Modell Foundation’s (JMF) Ten Warning Signs are widely used for early detection; however, their diagnostic sensitivity...

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Bibliografiske detaljer

Principais autores:	Alaaddin Yorulmaz, Ali Şahin, Gamze Sonmez, Fadime Ceyda Eldeniz, Yahya Gül, Mehmet Ali Karaselek, Şükrü Nail Güler, Sevgi Keleş, İsmail Reisli
Format:	Artigo
Sprog:	Inglês
Udgivet:	MDPI AG 2025-09-01
Serier:	Children
Fag:	inborn errors of immunity Jeffrey Modell Foundation machine learning support vector machine clinical decision support systems
Online adgang:	https://www.mdpi.com/2227-9067/12/9/1259
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