UCLA Helps Caregivers Predict and Prevent Asthma Attacks in Children
The UCLA School of Medicine’s BREATHE (Biomedical REAl-Time Health Evaluation) project for pediatric asthma wanted to know if real-time data collection could pinpoint ways to predict and prevent asthma attacks in children. To explore this, the School of Medicine Research Computing (RC) Team designed an Amazon Web Services (AWS) environment that uses 24/7 data collection, machine-learning algorithms, and heavy computation that their on-premises cluster could not handle.
Research Computing worked with teams across UCLA and USC to get the BREATHE platform up and running in the AWS Cloud. Unlike older, segmented computing solutions, the cloud computing environment enables the complex workflows behind BREATHE’s platform to operate in a centralized location.
The BREATHE platform collects environmental and physiological data on research subjects who wear Internet of Things (IoT) sensors. The platform processes the data and sends it back to the devices in the form of a recommendation or insight. Researchers hope the platform’s insights will help caregivers predict and prevent asthma attacks in children.
“By using AWS, we are able to equip researchers at the university with the tools to make a real impact for patients,” said Ben Nathan, CIO, UCLA David Geffen School of Medicine. “From compute to IoT to machine learning, the technology allows us to think bigger and answer questions we couldn’t before. More centralized access to technology equals more children we can help.”
Running BREATHE in the AWS Cloud enables:
- 24/7 data collection from IoT devices. Collected data includes heartrate, air quality factors, and more.
- Real-time algorithms to process data—integrating it with medical histories and other factors—and provide research subjects with recommendations.
- Post-analysis to improve machine-learning algorithms.
- Data sharing within UCLA and with partner institutions.
- Research scalability and flexibility, as the cloud environment can be easily updated as needed. For example, the project’s second phase will bring into the platform real-time environmental data from government agencies (e.g., EPA) and other services.
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