Guest Lecture: Jimeng Sun
Ehrlicher Room, 3100 North Quad
Doctor AI— Computational Phenotyping from Electronic Health Records
Can Artificial Intelligence (AI) help improve healthcare? The central task for AI in medicine is to develop efficient and accurate phenotyping methods that can transform large volume of complex and noisy electronic health records (EHR) into meaningful clinical patterns (or phenotypes). We will describe several promising phenotyping techniques to support various healthcare applications such as disease risk prediction, treatment recommendation and phenotype discovery:
- Deep learning: We present a series of neural network methods that can handle temporal patterns, leverage existing medical knowledge, exploit complex relationships within EHR data.
- Tensor factorization: We introduce a set of tensor factorization methods that are designed to model large volume of unlabeled and time-evolving EHR data to extract novel phenotypes.
Jimeng Sun is an Associate Professor of College of Computing at Georgia Tech. Prior to Georgia Tech, he was a researcher at IBM TJ Watson Research Center. His research focuses on health analytics and machine learning, especially in designing tensor factorizations, deep learning methods, and large-scale predictive modeling systems. He published over 120 papers and filed over 20 patents (5 granted). He was named Top 100 AI Leaders in Drug Discovery and Advanced Healthcare. He has received SDM/IBM early career research award 2017, SC’18 best student paper award, ICDM best research paper award in 2008, SDM best research paper award in 2007, and KDD Dissertation runner-up award in 2008. Dr. Sun received B.S. and M.Phil. in Computer Science from Hong Kong University of Science and Technology in 2002 and 2003, M.Sc and PhD in Computer Science from Carnegie Mellon University in 2006 and 2007.