Learning “Healthy” Models for Healthcare
Marzyeh Ghassemi, Assistant Professor, Faculties of Computer Science & Medicine, University of Toronto Faculty Member, Vector Institute
Professor Ghassemi will discuss the current state and potential of machine learning for health.
Marzyeh Ghassemi is an Assistant Professor at the University of Toronto in Computer Science and Medicine, affiliated with the Vector Institute. Professor Ghassemi has a well-established academic track record in personal research contributions across computer science and clinical venues. Her PhD research at MIT focused on creating and applying machine learning algorithms towards improved prediction and stratification of relevant human risks with clinical collaborations at Beth Israel Deaconess Medical Center and Massachusetts General Hospital, encompassing unsupervised learning, supervised learning, and structured prediction. Her work has been applied to estimating the physiological state of patients during critical illnesses, modeling the need for a clinical intervention, and diagnosing phonotraumatic voice disorders from wearable sensor data.
Approaching the Promise of Artificial Intelligence & Big Data in Healthcare
Health systems, services and policy researchers are increasingly interested in the potential for AI and Big Data to address challenges ranging from complex societal problems to those relating to individual care.
For this reason, the Health Services, Systems & Policy Seminar Series at the Institute of Health Policy, Management and Evaluation (IHPME), University of Toronto will focus this year on the potential and limitations of AI and Big Data to improve healthcare.
Canadian and international researchers and practitioners from an array of disciplines including computer science, decision sciences, predictive analytics, and bioinformatics will join us to discuss applications of AI and big data to issues ranging from genomics, radiology, public health and hospital scheduling to blood transfusion.
Please consider forwarding this information to any colleagues who might be interested.