# OBJECTIVE HOME MANAGEMENT OF PEDIATRIC ASTHMA EXACERBATION USING MOBILE TECHNOLOGY AND MACHINE LEARNING

> **NIH NIH R41** · AUSCULTECH DX, LLC · 2020 · $257,878

## Abstract

PROJECT SUMMARY
Asthma is the most common chronic pediatric disease in the United States, affecting 6.2 million or 1 in
12 children. Despite advances in the management of childhood asthma, asthma exacerbation results in
approximately 550,000 emergency department (ED) visits, 80,000 hospitalizations, and hundreds of
premature deaths each year. Early symptoms of asthma exacerbation, especially in children, are
nonspecific and are unfortunately often not recognized by parents as asthma until the child
demonstrates more severe symptoms, enough to require emergent care. The long-term goal of this
STTR initiative is to empower parents to initiate timely therapy for acute asthma by supplementing
their subjective assessment with an objective measure of acute asthma severity. The mobile
technology we propose to develop, test and deploy toward this goal will use digital signal processing
(DSP) and machine learning (ML) to determine three distinct severity zones (corresponding to the
green, yellow and red zones on the asthma action plan) allowing parents to follow asthma action plans
accurately. The resulting improved and timely home-based management of childhood asthma should
reduce current excessive ED utilization and unacceptably high rates of morbidity and mortality. The
proposed technology is inspired by and based on the pediatric asthma severity score (PASS). PASS is
used in many pediatric EDs for objective assessment of acute asthma severity to aid management of
acute asthma and critical discharge and hospitalization decisions. The components of PASS are five
well-studied and validated clinical parameters. Our goal in the proposed project is to develop new
technology that enables parents to make similar measurements in the home setting and map those to
the 3 color-coded zones for easier execution of asthma action plans. The 2 specific aims of the project
are to 1) build a pediatric asthma database with ground-truth clinical findings, and 2) develop and
validate DSP and ML algorithms for automated assessment of acute asthma severity. The successful
completion of these aims will result in a validated technology suitable for objective, home-based
assessment of acute asthma severity. Such assessment is currently possible but only in medical
facilities by trained medical staff. Our Phase II goals will be to (a) further improve and deploy the
technology in homes and (b) conduct a prospective trial measuring its feasibility, utilization and
effectiveness in controlling asthma emergencies and costs. In Phase II, we will also pursue the
necessary regulatory approvals.

## Key facts

- **NIH application ID:** 10010457
- **Project number:** 1R41NR019735-01
- **Recipient organization:** AUSCULTECH DX, LLC
- **Principal Investigator:** Shilpa Patel
- **Activity code:** R41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $257,878
- **Award type:** 1
- **Project period:** 2020-08-19 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10010457

## Citation

> US National Institutes of Health, RePORTER application 10010457, OBJECTIVE HOME MANAGEMENT OF PEDIATRIC ASTHMA EXACERBATION USING MOBILE TECHNOLOGY AND MACHINE LEARNING (1R41NR019735-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10010457. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*