Despite critical illness remaining a leading cause of morbidity and mortality, progress in critical care research has been impeded by fragmented data systems and difficulties with granular, time-series data standardization. In this talk, the application of machine learning and data science in the intensive care unit (ICU) will be explored, with particular emphasis on the CarpeDiem framework for patient trajectory modeling and the development of the Common Longitudinal ICU data Format Consortium.

Speaker Bio

Catherine A. Gao, MD, MS, of the Northwestern University Feinberg School of Medicine, completed medical training at the University of Michigan followed by internal medicine residency at Yale and pulmonary critical care fellowship at Northwestern. Her research focuses on using machine learning and artificial intelligence to understand how we can improve the care of critically ill patients. 

Accreditation

The University of Rochester School of Medicine and Dentistry is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.

Certification

The University of Rochester School of Medicine and Dentistry designates this live activity for a maximum of 1.00 AMA PRA Category 1 Credits™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.