- Prof Richard Clayton, University of Sheffield
- Prof Peter Challenor, University of Exeter
- Peter Coveney, Professor of Physical Chemistry
- Alfons Hoekstra, Associate Professor, University of Amsterdam
An essential step in making computational approaches in biomedicine actionable is to ensure that the findings derived from it are indeed robust, and that all relevant elements of uncertainty are quantified and accounted for. This uncertainty can for instance originate from input data sources (e.g., noise or bias in experimental devices) and propagate as the workflow proceeds from one computational or data analysis step to another, leading to confidence intervals in the final result.
In this symposium we are seeking contributions that study uncertainty quantification (UQ) in the context of biomedicine. This includes the study of either epistemic or aleatory uncertainty, intrusive and non-intrusive computational approaches to quantify uncertainty, specific applications of UQ in challenging biomedical settings, and tools and techniques that help incorporate UQ on an application-agnostic level.
|Sensitivity and uncertainty analysis of cardiac cell models with Gaussian process emulators
|11:20||Ritabrata Dutta||Pathological Test for Cardio/cerebrovascular diseases: Platelets dynamics and Approximate Bayesian computation|
|11:35||Alberto Marzo||Use of a Gaussian process emulator and 1D circulation model to characterize cardiovascular pathologies and guide clinical treatment|
|Safety, Reproducibility, Performance: Accelerating cancer drug discovery with ML and HPC technologies|
|12:10||End of Session|