AI-based Platform for Population-level Social Isolation Detection and Prediction

Social isolation is a serious public health issue which has many negative effects on quality of life and well-being of individuals. This research project aims to develop and test a Minimally Viable technology platform (MVP) to tackle population-level social isolation. This platform is designed to collect and analyze surveys from the users and detect socially isolated people and identify individuals at risk of isolation in a community using AI and social network analysis techniques. It is capable of analyzing real-time data obtained from the users through multiple channels such as web portal or mobile app. Our approach is to map the network to an attributed weighted multi-dimensional social graph and use graph theory, network science, and AI techniques.

Intern: 
Bahareh Rahmatikargar
Faculty Supervisor: 
Pooya Moradian Zadeh
Province: 
Ontario
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