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SUMMARY:Efficient Probabilistic Model Personalization Integrating Uncertai
 nty on Data and Parameters: Application to Eikonal-Diffusion Models in Car
 diac Electrophysiolo - Konukoglu\, E (Microsoft Research)
DTSTART:20110822T153000Z
DTEND:20110822T160000Z
UID:TALK32432@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:Biophysical models are increasingly used for medical applicati
 ons at the organ scale. However\, model predictions are rarely associated 
 with a confidence measure although there are important sources of uncertai
 nty in computational physiology methods. For instance\, the sparsity and n
 oise of the clinical data used to adjust the model parameters (personaliza
 tion)\, and the difficulty in modeling accurately soft tissue physiology. 
 The recent theoretical progresses in stochastic models make their use comp
 utationally tractable\, but there is still a challenge in estimating patie
 nt-specific parameters with such models. \n\nIn this talk I will describe 
 an efficient Bayesian inference method for model personalization (paramete
 r estimation) using polynomial chaos and compressed sensing. I will demons
 trate the method in the context of cardiac electrophysiology and show how 
 this can help in quantifying the impact of the data characteristics and un
 certainty on the personalization (and thus prediction) results.\n\nDescrib
 ed method can be beneficial for the clinical use of personalized models as
  it explicitly takes into account the uncertainties on the data and the mo
 del parameters while still enabling simulations that can be used to optimi
 ze treatment. Such uncertainty handling can be pivotal for the proper use 
 of modeling as a clinical tool\, because there is a crucial requirement to
  know the confidence one can have in personalized models.\n\n
LOCATION:Seminar Room 1\, Newton Institute
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