It is widely believed that deep learning and artificial intelligence techniques will fundamentally change healthcare industries. Even though recent development in deep learning has achieved successes in many applications, such as computer vision, natural language processing, speech recognition and so on, healthcare applications pose many significantly different challenges to existing deep learning models. Examples include but not are limited to interpretations for prediction, heterogeneity in data, missing value, multi-rate multi-resolution data, big and small data, and privacy issues. In this tutorial, we will discuss a series of problems in healthcare that can benefit from deep learning models, the challenges as well as recent advances in addressing those. We will also include data sets and demos of working systems.
● Introduce diverse types of healthcare data
● Overview different ML problems for healthcare applications
● Explain the technical challenges for applying deep learning for healthcare applications
Recently, there are substantially growing interest in ML in health applications, thanks to the heterogeneous healthcare data such as electronic health records, medical images, clinical notes and continuous monitoring data. The analytic problems in healthcare pose many unique ML challenges that are worth discussing.
The tutorial is targeted at researchers in machine learning as well as researchers working on the health-related applications. It will also attract a broader of audience who work on applying deep learning models to applications with heterogeneous data. The prerequisites include graduate-level machine learning classes and basic knowledge on deep learning.
Slides: [PDF]
Related publications from the presenters:
Yan Liu, University of Southern California ( yanliu.cs@usc.edu ) Dr. Liu is an associate professor in Computer Science Department at the University of Southern California from 2010. Before that, she was a Research Staff Member at IBM Research from 2006-2010. She received her M.Sc. and Ph.D. degrees from Carnegie Mellon University in 2004 and 2006. Her research interests are developing scalable machine learning algorithms with applications to health and biology applications, social media analysis, and climate modeling. She has received several awards, including NSF CAREER award, Okawa research award, ACM Dissertation Award Honorable Mention and Best Application Paper Award at SDM 2007, and has won faculty award from Facebook, Yahoo, IBM, Samsung, and ExxonMobil. She has served as program committee or senior program committee in all top conferences in machine learning, such as ICML, NIPS, UAI, AIStat, KDD and so on. She has also co-organized several workshops and gave tutorials in these conferences.
Jimeng Sun, Georgia Institute of Technology ( jsun@cc.gatech.edu ), Dr. 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: Children's Healthcare of Atlanta, Vanderbilt university medical center, Mass General hospital, Sutter Health, Geisinger, Northwestern and UCB. He published over 120 papers and filed over 20 patents (5 granted). He has received 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.