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

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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 account for overlapping subtypes, subtype evolution, severity, nor do they include social determinants of health (SDOH). As such, if we are to reduce the burden of asthma at both the individual and population level, we must improve asthma subtype characterization to help clinicians craft more personalized primary and emergency care. To date, however, asthma subtyping studies have been limited by small sample sizes, ignored temporal information, and/or focused on individual or a handful of sites. The proliferation of large clinical research networks (CRNs) with real-world data (RWD) from electronic health records (EHRs), combined with advancements in machine learning offer unique opportunities to improve subtyping of pediatric asthma patients. Our study team’s preliminary analysis of asthma exacerbations in the OneFlorida+ CRN using only structured data found five pediatric asthma subtypes which varied by race/ethnicity, severity, digital biomarkers, and comorbidities. Our work supports that there is further heterogeneity in pediatric asthma beyond the classically defined subtypes of allergic vs non-allergic. In this project, we will leverage the OneFlorida+ CRN’s large repository of RWD (covering nearly 20 million patients in the southeast) and a novel privacy-preserving federated machine learning-based framework to: (1) identify pediatric asthma patients, their severity, subtypes, and disease progression (i.e., progression subtypes), and (2) fine-tune those global models to local OneFlorida+ sites with site-specific data to account for between-site heterogeneity. In addition to structured EHR data, we will include spatiotemporally linked environmental data and use natural language processing to include clinical note data such as symptoms and SDOH. To guide our work and inform implementation efforts, we will establish a stakeholder advisory committee with pediatric asthma, healthcare system, and public health stakeholders, and conduct focus groups with local OneFlorida+ site clinicians to develop and test EHR prototypes that integrate subtype data. Pediatric asthma progression subtypes built using RWD from diverse populations combined with stakeholder engagement will move the field closer to precision primary and emergency care that improves outcomes. Our novel privacy-preserving federated machine learning methods address several challenges of RWD analysis and will be a generalizable framework for other CRNs to adopt, facilitating widespread dissemination of this work, and paving a path forward for progression subtype analyses of other chronic diseases.