Fishe Lab

University of Florida

EMERGENCY MEDICINE

Gainesville · United States

NIH-funded
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NIH R01 · 2024

Identifying pediatric asthma subtypes using novel privacy-preserving federated machine learning methods

ABSTRACT Asthma affects nearly 6 million children in the United States, and on average, each child with asthma experiences at least one exacerbation per year. Pediatric asthma accounts for over 790,000 emergency department visits, 64,000 hospitalizations, and nearly $6 billion in direct healthcare costs annually. Asthma disproportionately affects minority children, who are at risk for more severe outcomes. Asthma is a heterogeneous disease with a range of etiologies, triggers, severities, and treatment responses (i.e., subtypes). Despite that well-known heterogeneity, asthma subtypes are largely confined to a simple dichotomous classification of allergic versus non-allergic, which does not…

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