Learning to Assess Disease and Health In Your Home

Abstract: The future of healthcare lies in delivering comprehensive medical services to patients in their own homes. As the global population ages and chronic diseases become increasingly prevalent, objective, longitudinal and reliable health and disease assessment at home becomes crucial for early detection and prevention of hospitalization. In this talk, I will present new learning methods with everyday devices for in-home healthcare. I will first describe a simple self-supervised framework for remote human vitals sensing just using daily smartphones. I will then introduce an AI-powered digital biomarker for Parkinson’s disease that detects the disease, estimates its severity, and tracks its progression using nocturnal breathing signals. They showcase the potential of AI-based in-home assessment for various diseases and human health sensing, enabling remote monitoring of health-related conditions, timely care and enhancing patient outcomes.

Speaker bio: Yuzhe Yang is a PhD candidate in computer science at MIT. He received his B.S. with honors in EECS from Peking University. His research interests include machine learning, and AI for human disease, health and medicine. His works on AI-enabled biomarkers for Parkinson’s disease were named as Ten Notable Advances in 2022 by Nature Medicine, and Ten Crucial Advances in Movement Disorders in 2022 by The Lancet Neurology. His research has been published in Nature Medicine, Science Translational Medicine, NeurIPS, ICML, ICLR, CVPR, and UbiComp. His works have been recognized by the MathWorks Fellowship, Takeda Fellowship, Baidu PhD Scholarship, and media coverage from MIT Tech Review, Wall Street Journal, Forbes, BBC, The Washington Post, etc.