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 individual’s 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 tools—tools 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. Saria also serves on the editorial board of the Journal of Machine Learning Research. Her research focuses on developing next generation diagnostic and treatment planning tools that leverage statistical methods to individualize care. Prior to joining Hopkins in 2012, Saria received her PhD from Stanford University working with Prof. Daphne Koller. 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.