Abstract: 

In this tutorial, we present deep learning methods and their applications in computational healthcare, specifically focusing on clinical representation learning, predictive modeling, clinical trial modeling and drug development.
We will also introduce different types of data in healthcare including structured electronic health records, unstructured clinical notes, medical images, clinical trial description, chemical compounds and medical knowledge base.
This tutorial is intended for data scientists, engineers and researchers who are interested in applying deep learning methods to healthcare, and prerequisite knowledge include basic machine knowledge. The first half will be spent on introducing the nature of health data, basic deep learning methods and their application in healthcare. In the second half, we will focus on challenges specific to computational healthcare, and introduce advanced deep learning methods ranging from deep phenotyping, chronic disease prediction, rare disease detection, patient-trial matching, and molecule generation and drug property prediction. The tutorial will be concluded with open problems and a Q&A session.

Bio: 

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 data mining, especially in designing tensor factorizations, deep learning methods, and large-scale predictive modeling systems. Dr. Sun has been collaborating with many healthcare organizations.
He published over 120 papers and filed over 20 patents (5 granted). He has received SDM/IBM early career research award 2017, 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, PhD in Computer Science from Carnegie Mellon University in 2007 advised by Christos Faloutsos.