Machine Learning for Survival Analysis

Survival analysis is a subfield of statistics where the goal is to analyze and model data where the outcome is the time until an event of interest occurs. One of the main challenges in this context is the presence of instances whose event outcomes become unobservable after a certain time point or when some instances do not experience any event during the monitoring period. This so-called censoring can be handled most effectively using survival analysis techniques. Traditionally, statistical approaches have been widely developed in the literature to overcome the issue of censoring. In addition, many machine learning algorithms have been adapted to deal with such censored data and tackle other challenging…

• NS
  National Science Foundation

  Award: IIS-1707498, IIS-1619028 and IIS-1838730