Dr. Georgia Tourassi

Pathology reports are a primary source of information for cancer registries which process high volumes of free-text reports annually. Information extraction and coding is a manual, labor-intensive process. In this talk I will discuss the latest artificial intelligence technologies, presenting both theoretical and practical perspectives that are relevant to natural language processing of clinical pathology reports for computational cancer phenotyping. Using different deep learning architectures, I will present benchmark studies for various information extraction tasks and discuss their importance in supporting a comprehensive and scalable national cancer surveillance program.

Biography:

Georgia (Gina) Tourassi is the Director of the National Center for Computational Sciences at the Oak Ridge National Laboratory. Concurrently, she holds appointments as an adjunct Professor of Radiology at Duke University and the University of Tennessee and as a joint UT-ORNL Professor of the Bredesen Center Data Science Program at the University of Tennessee at Knoxville. Her scholarly work includes 13 US patents and innovation disclosures and more than 250 peer-reviewed journal articles, conference proceedings articles, editorials, and book chapters. She is elected Fellow of the American Institute of Medical and Biological Engineering, the American Association of Medical Physicists, and the International Society for Optics and Photonics. She is Senior member and Distinguished Lecturer of the IEEE Engineering in Medicine and Biology Society. Her research interests include artificial intelligence in biomedicine,  biomedical informatics, clinical decision support, digital epidemiology, and data-driven biomedical discovery. Dr. Tourassi holds a B.S. degree in Physics from Aristotle University of Thessaloniki, Greece and a Ph.D. in Biomedical Engineering from Duke University.