PhD Thesis Proposal - Hsien-Te Kao

Committee: Emilio Ferrara (Chair), Kristina Lerman, Phebe Vayanos, Souti Chattopadhyay, Ruishan Liu  
 
Date and Time: Friday, February 2, 2024, 1:00 PM - 2:30 PM PST - RTH 115
 
Title: Cold Start Prediction in Personalized mHealth  
 
Abstract: Mobile health has brought fundamental changes to the healthcare industry, offering new hope in addressing growing healthcare expenditures, opportunity costs, and labor shortages. Machine learning is driving mobile health towards decentralized healthcare by automating health monitoring, diagnosis, and treatment. Personalized mobile health systems are a key component in advancing patient-centric healthcare, but these systems remain unfeasible outside of hospital settings because personal health data is largely inaccessible, uncollectible, and regulated. In this proposal, we introduce a personalized mobile health system to predict individual health status without user context through a set of mobile, wearable, and ubiquitous technologies. The model leverages collaborative filtering to replace missing user context with learned similar group characteristics, where user similarity is captured through multiple dimensions of cognitive appraisal based on a combination of psychology theories. The system eliminates user dependence through passive feedback that satisfies real-world constraints. Our preliminary results demonstrate a proof-of-concept system.

Location: Ronald Tutor Hall of Engineering (RTH) - 115

Audiences: Everyone Is Invited

Contact: CS Events