Talk by Russ Greiner, University of Alberta

Hosts: Siamak Ravanbakhsh and Mark Schmidt

Titel: An Effective Way to Estimate an Individual's Survival Distribution

Abstract:

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 mechanic device breaks, and customer churn. This presentation focuses the most common situation: estimating the time until a patient dies.

An accurate model of a patient’s individual survival distribution can help determine the appropriate treatment and care of terminal patients. The common practice of estimating such survival distributions uses only population averages for (say) the site and stage of cancer; however, this is not very precise, as it ignores many important individual differences among patients. This paper describes a novel technique, PSSP (patient-specific survival prediction), for estimating a patient’s individual survival curve, based on the characteristics of that specific patient, using a model that was learned from earlier patients. We describe how PSSP works, and explain how PSSP differs from the more standard tools for survival analysis (Kaplan-Meier, Cox Proportional Hazard, etc). We also show that PSSP is "calibrated", which means that its probabilistic estimates are meaningful. Finally, we demonstrate, over many real-world datasets (various cancers, and liver transplantation), that PSSP provides survival estimates that are helpful for patients, clinicians and researchers.

Link to Bio: https://webdocs.cs.ualberta.ca/~rgreiner/index.php?section=Presentations#PSSP