[Lisa_seminaires] Fwd: [LabRL] Seminar by Russ Greiner, University of Alberta, Tu 2:30pm

Aaron Courville aaron.courville at gmail.com
Jeu 23 Aou 09:39:13 EDT 2012


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


---------- Forwarded message ----------
> From: Doina Precup <dprecup at cs.mcgill.ca>
> Date: August 23, 2012 9:13:40 AM EDT
> To: labrl at 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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-- 
Aaron C. Courville
Département d’Informatique et
de recherche opérationnelle
Université de Montréal
email:Aaron.Courville at gmail.com
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