Integrating clinical phenotypes, impulse oscillometry and fractional exhaled nitric oxide: a robust machine learning model for early asthma prediction in preschool children.

Abstract excerpt

BACKGROUND: Preschool wheeze diagnoses prove challenging due to the significant heterogeneity and lack of objective laboratory markers. To design and validate a predictive model powered by machine learning, which integrates impulse oscillometry (IOS), fractional of exhaled nitric oxide (FeNO), and clinical features to facilitate early asthma prediction in preschool children. METHODS: A total of 264 preschool...

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