UCLA

Pervasive health is an emerging field of research aimed at providing health monitoring to anyone at any time and anywhere. In contrast to existing healthcare systems, this paradigm is centered on prevention, wellness maintenance, and proactive care outside the hospital setting. In recent years, advances in sensing technologies and machine learning, as well as wide adoption of smart devices, have led to the development of successful pervasive health solutions such as daily activity monitoring.

Despite this growth, the full potential of pervasive health is yet to be unlocked. In this dissertation, we make several contributions to the application and methodological sides of pervasive health to advance it closer to its promises. We investigate design, implementation, and deployment of a highly context-aware and real-time solution for the complex application of pediatric asthma management, trying to predict the exacerbation of asthma ahead of time. On the methodological side, we investigate unmet data modeling challenges that arise in building a highly context-aware pervasive health solution and propose novel approaches to address them. In particular, we approach the challenges of integrative and interpretable modeling of heterogeneous health data, transferring and adapting models to new domains/individuals, and tackling sparsity of labels in real-world settings.