Machine Learning for Survival Analysis: Theory, Algorithms and Applications

author: Yan Li, University of Michigan
author: Chandan K. Reddy, Department of Computer Science, Virginia Polytechnic Institute and State University
published: Nov. 21, 2017,   recorded: August 2017,   views: 972

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.

 Watch videos:   (click on thumbnail to launch)

Watch Part 1
Part 1 1:48:51
Watch Part 2
Part 2 1:33:07


Due to the advancements in various data acquisition and storage technologies, different disciplines have attained the ability to not only accumulate a wide variety of data but also to monitor observations over longer time periods. In many real-world applications, the primary objective of monitoring these observations is to estimate when a particular event of interest will occur in the future. One of the major difficulties in handling such problem is the presence of censoring, i.e., the event of interests is unobservable in some instance which is either because of time limitation or losing track. Due to censoring, standard statistical and machine learning based predictive models cannot readily be applied to analyze the data. An important subfield of statistics called survival analysis provides different mechanisms to handle such censored data problems. In addition to the presence of censoring, such time-to-event data also encounters several other research challenges such as instance/feature correlations, high-dimensionality, temporal dependencies, and difficulty in acquiring sufficient event data in a reasonable amount of time. To tackle such practical concerns, the data mining and machine learning communities have started to develop more sophisticated and effective algorithms that either complement or compete with the traditional statistical methods in survival analysis. In spite of the importance of this problem and relevance to real-world applications, this research topic is scattered across various disciplines. In this tutorial, we will provide a comprehensive and structured overview of both statistical and machine learning based survival analysis methods along with different applications. We will also discuss the commonly used evaluation metrics and other related topics. The material will be coherently organized and presented to help the audience get a clear picture of both the fundamentals and the state-of-the-art techniques.

Link to tutorial:

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: