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

> **NIH NIH R01** · UNIVERSITY OF FLORIDA · 2024 · $677,396

## Abstract

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...

## Key facts

- **NIH application ID:** 10918182
- **Project number:** 5R01HL169277-02
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Jennifer Noel Fishe
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $677,396
- **Award type:** 5
- **Project period:** 2023-09-01 → 2028-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10918182

## Citation

> US National Institutes of Health, RePORTER application 10918182, Identifying pediatric asthma subtypes using novel privacy-preserving federated machine learning methods (5R01HL169277-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10918182. Licensed CC0.

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