Individualizing Healthcare with Machine Learning
Seminar | March 14 | 4-5 p.m. | Soda Hall, 306 Soda Hall - HP Auditorium
Suchi Saria, Assistant Professor, Johns Hopkins University
Healthcare is rapidly becoming a data-intensive discipline, driven by increasing digitization of health data, novel measurement technologies, and new policy-based incentives. Critical decisions about whom and how to treat can be made more precisely by layering an individuals data over that from a population. In this talk, I will begin by introducing the types of health data currently being collected and the challenges associated with learning models from these data. Next, I will describe new techniques that leverage probabilistic methods and counterfactual reasoning for tackling the aforementioned challenges. Finally, I will introduce areas where statistical machine-learning techniques are leading to new classes of computational diagnostic and treatment planning toolstools that tease out subtle information from messy observational datasets, and provide reliable inferences given detailed context about the individual patient.
Bio: Suchi Saria is the John C. Malone Assistant Professor of computer science, statistics and health policy at Johns Hopkins University. She is also the founding Research Director of the Malone Center for Engineering in Healthcare at Hopkins. Her research focuses on developing next generation diagnostic and treatment planning tools that leverage statistical methods to individualize care. Towards this, her methodological work focuses on questions such as: How can we combine different sources of information with prior knowledge to derive actionable inferences? How can we characterize and improve reliability of the resulting inferences in challenging real-world settings? How can we support decision-making in safety-critical domains?
Saria joined Hopkins in 2012. Prior to that, she received her PhD from Stanford University working with Prof. Daphne Koller. Her work has received recognition in the form of two cover articles in Science Translational Medicine (2010, 2015), paper awards by the the Association for Uncertainty in Artificial Intelligence (2007) and the American Medical Informatics Association (2011), an Annual Scientific Award by the Society of Critical Care Medicine (2014), a Rambus Fellowship (2004-2010), an NSF Computing Innovation Fellowship (2011), selection by IEEE Intelligent Systems to Artificial Intelligences 10 to Watch (2015), the DARPA Young Faculty Award (2016), MIT Technology Reviews 35 Innovators under 35 (2017), and the Sloan Research Fellowship (2018). She has given over 80 invited talks including presentations at the National Academy of Sciences, National Academy of Engineering, and the White House Frontiers Meeting. In 2017, her work was among four research contributions presented by Dr. France Córdova, Director of the National Science Foundation to Congress Commerce, Justice Science Appropriations Committee.