Hi All,
As the message below states, Russ Greiner, Professor at the University of Alberta, is visiting us at McGill next Tuesday, August 28. He will give a talk on his research (details below).
Cheers, Aaron
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From: Doina Precup dprecup@cs.mcgill.ca Date: August 23, 2012 9:13:40 AM EDT To: labrl@cs.mcgill.ca Subject: [LabRL] Seminar by Russ Greiner, University of Alberta, Tu 2:30pm
Hi everyone,
Russ Greiner, Professor at the University of Alberta, is visiting us at
McGill next Tuesday, August 28. He will give a talk on his research (details below). If you want to meet with Russ, please let me know.
Title: Learning Patient-Specific Survival Distributions as a Sequence
of Dependent Regressors
Time and Place: Tuesday Aug. 28, 2:30pm, McConnell Engineering
Building, room 437
Abstract: An accurate model of patient survival time can help in the
treatment and care of patients. The common practice of providing survival time estimates based only on population averages for the site and stage of the disease ignores many important individual differences among patients. Here, we present a novel machine learning algorithm, PSSP (for “patient-specific survival predictor"), for learning patient-specific survival time distribution based on patient attributes, such as blood tests and clinical assessments. The predicted distribution can be regarded as a personalized version of Kaplan-Meier curve, and can be used as a tool for doctors to visualize the survival rate of individual patients. PSSP can also easily incorporate the time-varying effects of prognostic factors and handle censored survival times. When tested on a cohort of more than 2000 cancer patients from the Alberta Cancer Registry, our method gives survival time predictions that are much more accurate than popular survival analysis models such as the Cox and Aalen regression models. Our results show that using patient-specific attributes can reduce the prediction error on survival time by as much as 20% when compared to using cancer site and stage only. We anticipate this same technology can be used for learning and predicting personalized Kaplan-Meier curves for patients suffering from other diseases.
Joint work with Chun-Nam Yu, Vickie Baracos
Website: http://pssp.srv.ualberta.ca/
Best, Doina
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