Doctoral Thesis: Machine Learning Approaches for Healthcare Discovery, Delivery, and Equity
32-D463 (Star)
By: Yuzhe Yang
Supervisor: Dina Kati
Details
- Date: Tuesday, April 30
- Time: 10:00 am - 11:30 am
- Category: Thesis Defense
- Location: 32-D463 (Star)
Additional Location Details:
Will be hybrid and have a Zoom link, please contact Yuzhe (yuzhe@mit.edu) for the link
Abstract: Today’s clinical systems frequently exhibit delayed diagnoses, sporadic patient visits, and unequal access to care. Can we identify chronic diseases earlier, potentially before they manifest clinically? Furthermore, can we bring comprehensive medical assessments into patient’s own homes to ensure accessible care for all? In this talk, I will present machine learning approaches to bridge the persistent gaps in healthcare discovery, delivery, and equity. I will first introduce an AI-powered digital biomarker that detects Parkinson’s disease multiple years before clinical diagnosis, using just nocturnal breathing signals. I will then discuss a simple self-supervised framework for contactless measurement of human vital signs using smartphones. Finally, I will discuss principled methods to achieve equitable healthcare decision-making systems across diverse subpopulations and distribution shifts for real-world deployment.