Suchi Saria directs the Machine Learning and Healthcare Lab and is the founding research director of the Malone Center for Engineering in Healthcare. Saria’s goal is to use sophisticated computer science and the deluge of data available in health care and other settings to individualize patient care and to save lives. Her pioneering work centers on enabling new classes of diagnostic and treatment planning tools for health care—tools that use statistical machine-learning techniques to tease out subtle information from “messy” observational datasets, and provide reliable inferences for individualizing care decisions. Saria’s work provides an entry point into a future in which the data collected from a large number of patients may reliably inform physicians about the best treatment plans for individual patients. For instance, algorithms that she created are being used today in hospitals to predict with startling accuracy which patients will succumb to deadly sepsis (a condition that annually kills more people than breast and prostate cancer combined)— work that led to her being named one of Popular Science magazine’s “Brilliant 10” (2016); one of MIT’s “35 Innovators Under 35” (2017); and a member of the World Economic Forum’s Young Global Leaders (2018).
In order to expedite clinical diagnostics and advance precision patient care, innovative developments in algorithm development and imaging sciences, combined with improved understanding of the complex biology of cancer is crucial. This session will cover various developments, needs, and opportunities of expedited clinical decision-making.