1265 Welch Rd
Stanford, CA 94305
USA
PI, Alberta Machine Intelligence Institute
University of Alberta
Abstract:
An accurate model of a patient’s survival distribution can help determine the appropriate treatment and care of each terminal patient. 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 presentation 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 models are typically “calibrated”, which means that their 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.
Russ Greiner: Short Biography:
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), which won the ASTech Award for “Outstanding Leadership in Technology” in 2006. He has been Program Chair for the 2004 “Int’l Conf. on Machine Learning”, Conference Chair for 2006 “Int’l Conf. on Machine Learning”, Editor-in-Chief for “Computational Intelligence”, and is serving on the editorial boards of a number of other journals. He was elected a Fellow of the AAAI (Association for the Advancement of Artificial Intelligence) in 2007, and was awarded a McCalla Professorship in 2005-06 and a Killam Annual Professorship in 2007. He has published over 250 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) bio- and medical- informatics; (2) learning and using effective probabilistic models and (3) formal foundations of learnability.