Title: Inverse problems in medical modelling
Organizer(s): Sergei Pereverzyev and Sivananthan Sampath, Johann Radon Institute for Computational and Applied Mathematics (RICAM), Austrian Academy of Sciences
Speakers: Chiara Dalla Man, University of Padova, Padova, Italy
Estimating meal glucose rate of appearance in type 1 diabetes

Valeriya Naumova, RICAM
Extrapolation in variable RKHSs with application to the blood glucose reading

Nathan Gibson, Oregon State University
Time-domain electromagnetic interrogation of biological materials

Ryan A. Hass, Oregon State University
Comet tail artifacts in 2D computed tomography
Abstract: Current Medical Modeling either operates with physiological models, which need to be calibrated beforehand from available clinical data, or is based on direct data mining. In both cases, effective regularization techniques should be employed. In the first case an application of the regularization theory is expected since model calibration is a typical inverse problem, which is usually ill-posed. As to the data mining, it is also a traditional application area for the regularization theory, where essential progress has been accomplished recently through regularization learning algorithms. All these call for a platform for joint discussion by the inverse problems and medical modeling communities. The mini-symposium is aimed at providing such a platform. In particular, we are going to discuss the problems and findings that have appeared in the course of the project "DIAdvisor" devoted to the improvement of diabetes therapy.

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