Acute medical conditions need immediate attention, but early detection can require professional experience and specialized equipment that are unavailable at home. Consequently, babies with such conditions risk suffering damage from late interventions. We can leverage the world’s increasingly ubiquitous devices to improve the accessibility of health care outside the hospital, provided that we integrate a human-centered approach at every step of the process. In this talk, I will illustrate the importance of applying HCI techniques in medical ML problems through discuss two projects: a smartphone-based system to screen newborns for dangerous levels of jaundice, and an exploration on how machine learning can help an existing system better monitor infants with single ventricle heart disease.
Lilian de Greef is a graduating computer science PhD candidate at the University of Washington with an NSF fellowship and Microsoft Research PhD fellowship, advised by Shwetak Patel in the Ubiquitous Computing Lab. Within the broad spectrum of ubiquitous computing, her interests include computer vision, embedded systems, machine learning, HCI, and mHealth. Her research focuses on integrating HCI, ML, and computer vision to improve access to medical care with low-cost commodity hardware and mobile phones. Outcomes of her work are under active commercial development and have already begun to change medical standards of care.
Faculty Host: Jeff Bigham