Individualizing Healthcare with Machine Learning
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 my laboratory, we develop 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. I will give example disease areas where such tools are already beginning to show translational impact. In context, I will describe challenges associated with learning models from these data and new techniques that leverage probabilistic methods and counterfactual reasoning for tackling the aforementioned challenges.
Date: 30 November 2018, 15:30 (Friday, 8th week, Michaelmas 2018)
Venue: 24-29 St Giles', 24-29 St Giles' OX1 3LB
Venue Details: Department of Statistics, Large Lecture Theatre
Speaker: Professor Suchi Saria (Department of Computer Science, Johns Hopkins University, USA)
Organising department: Department of Statistics
Organisers: Beverley Lane (Department of Statistics, University of Oxford), Professor Yee Whye Teh (Department of Statistics, University of Oxford)
Organiser contact email address: lane@stats.ox.ac.uk
Host: Professor Yee Whye Teh (Department of Statistics, University of Oxford)
Part of: Distinguished Speaker Seminar
Booking required?: Not required
Audience: Members of the University only
Editor: Beverley Lane