Event Details

PhD Dissertation Defense - Hsien-Te Kao

Tue, May 13, 2025
2:00 PM - 4:00 PM
Location: GCS 202C
Title: Cold Start Prediction in Personalizd Health
 
Date and Time: Tuesday, May 13th, 2025 | 2:00p - 4:00p
 
Location: GCS 202C
 
Committee Members: Emilio Ferrara (Chair), Kristina Lerman, Maryam M. Shanechi
 
Abstract: Mobile health (mHealth) has transformed healthcare delivery by using mobile technologies, wearable sensors, and machine learning to expand access, especially for populations facing geographic, economic, or clinical barriers. By enabling passive and continuous data collection, mHealth systems support early detection, real-time prediction, and proactive management of a wide range of health conditions through sensor-driven machine learning. Personalized mHealth extends these capabilities by integrating individual-level modeling and multi-source health records to improve model performance and support deeper understanding of individual health in their life contexts. Despite this progress, real-world deployment remains constrained by user reluctance, privacy concerns, and strict regulations that severely limit the availability of labeled individual health data. This dissertation presents a personalized mHealth framework designed to achieve mHealth predictions without health labels, addressing the cold-start problem. The work identifies key temporal segments that most influence model performance, introduces a cognitive appraisal-based similarity metric linking individuals through physiological signals and health labels, and demonstrates that five labels are sufficient for assigning users into their appraisal cohorts. It further shows that promising mHealth predictions can be achieved under cold-start conditions and uncovers how sociodemographic factors are associated with latent physiological and health patterns. The research contributes to foundational advances in theory-driven, label-efficient modeling for individualized health prediction. It also supports the development of practical mHealth systems capable of improving everyday health management beyond clinical settings.