Online
About this Event
The "survival prediction" task requires learning a model that can estimate the time until an event will happen for an instance; this differs from standard regression problems as the training survival dataset may include many "censored instances", which specify only a lower bound on that instance's true survival time. This framework is surprisingly common, as it includes many real-world situations, such as estimating the time until a customer defaults on a loan, until a game player advances to the next level, until a mechanical device breaks, and customer churn. [Note this framework allows that "true time" to be effectively infinite for some instances -- ie, some players may never advance, and some customers might not default, etc.] This presentation focuses on the most common situation: estimating the time until a patient dies.
An accurate estimate of a patient’s survival time can help determine the appropriate treatment and care of that patient. Some common approaches to survival analysis estimate a patient’s risk scores; others estimate a patient’s 5-year survival probability, or a population’s survival distribution; however, none of these provides a way to estimate an individual’s expected survival time. This motivates an alternative class of tools that can learn models that estimate a subject’s survival probability at each time -- ie, an individual survival distribution (ISD) -- from which one can then estimate that subject’s expected survival time. After describing such ISD models and explaining how they differ from standard models, this presentation then discusses standard ways to evaluate such models, then motivates and defines a novel approach, 'D-Calibration', which determines whether a model's probability estimates are meaningful. We also discuss how these measures differ, and use them to evaluate several ISD prediction tools over a range of real-world survival data sets -- demonstrating, in particular, that one tool, MTLR, provides survival estimates that are helpful for patients, clinicians and researchers.
After earning a PhD from Stanford, Russ Greiner worked in both academic and industrial research before settling at the University of Alberta where he is now a Professor in Computing Science and the founding Scientific Director of the Alberta Innovates Centre for Machine Learning (now Alberta Machine Intelligence Institute). He has published over 200 refereed papers and patents, most in the areas of machine learning and knowledge representation, including 4 that have been awarded Best Paper prizes. The main foci of his current work are (1) bioinformatics and medical informatics; (2) learning and using effective probabilistic models and (3) formal foundations of learnability.
Looking to build AI capacity? Need a speaker at your event?
