- : (30 min)
Dr. Computer, On Machine Learning Enabled, Data-Driven Healthcare
With the rapid growth of electronic health records (EHRs) and the advancement of machine learning (ML), ML-enabled data-driven healthcare is emerging. In this talk, I will introduce an ML platform we are building at Petuum Inc. for information distillation from EHRs and medical decision-making. I will discuss how to build a feature extraction pipeline that transforms unstructured and structured EHRs into canonical and readily actionable representations, and a machine learning framework that consumes these representations to assist physicians in making medical decisions, including diagnosis, similar-patient retrieval and treatment recommendation.
Dr. Eric Xing is a professor in the School of Computer Science at Carnegie Mellon University. His principal research interests lie in the development of machine learning and statistical methodology, and large-scale computational system and architecture, for solving problems involving automated learning, reasoning, and decision-making in high-dimensional, multimodal, and dynamic possible worlds in complex systems. Professor Xing received a Ph.D. in Molecular Biology from Rutgers University, and another Ph.D. in Computer Science from UC Berkeley. Professor Xing is an associate editor of the Journal of the American Statistical Association, Annals of Applied Statistics, the IEEE Transactions on Pattern Analysis and Machine Intelligence, the PLoS Journal of Computational Biology, and an Action Editor of the Machine Learning journal, and the Journal of Machine Learning Research. He is a member of the DARPA Information Science and Technology (ISAT) Advisory Group, a recipient of the NSF Career Award, the Alfred P. Sloan Research Fellowship, the United States Air Force Young Investigator Award, and the IBM Open Collaborative Research Faculty Award.