Room: Kilburn Building, Lecture Theatre 1.1

8:30-9:00 Arrivals

9:00-9:10 Welcome, 

9:10-10:10 TBC David Ginsbourger, University of Bern

10:10-10:40 Coffee Break

10:40-11:40 Emulating cohorts of cardiac digital twins using Gaussian Processes Chris Lanyon, University of Nottingham [slides]

11:40-12:40 TBC Ruby Sedgwick, Imperial College London [slides]

12:40-14:00 Lunch

14:00-15:00 Using GP emulation in cardiovascular modelling Mihaela Paun, University of Glasgow [slides]

15:00-15:30 Tea Break

15:30-16:30 TBC Lachlan Astfalck, University of Western Australia [slides]

Abstracts

TBC

Abstract

Emulating cohorts of cardiac digital twins using Gaussian Processes

Abstract

Mathematical models of individual patients (digital twins) have the capacity to be a key tool for personalised medicine which will allow us to make data driven clinical recommendations in real time. However, sufficiently detailed cardiac models are often computationally expensive to run and this limits their use in fast-paced clinical settings. One method of decreasing these computational costs is to use Gaussian process emulators (GPEs) as surrogate models. However, training a GPE still requires a large number of simulations of the original model. In this talk we will discuss methods of cohort calibration, where new patient models learn from a cohort of existing personalised GPEs. These cohort approaches reduce the simulation overheads whilst maintaining similar predictive power to GPEs trained on large sets of simulation data.

TBC

Abstract

Using GP emulation in cardiovascular modelling

Abstract

There have been impressive advances in the physical and mathematical modelling of complex systems in the last few decades, and increasingly cover areas that until recently have been regarded as elusive for the quantitative sciences. The focus of this talk will be on cardiovascular modelling, which has the potential to revolutionise personalised healthcare with accurate patient-specific risk prediction and evidence-based treatment through digital replicas. The coupled non-linear partial differential equations that the cardiovascular models are based on depend on unknown physical parameters, as well as initial and boundary conditions, which are estimated from data. A fundamental challenge is the fact that the parameter inference and uncertainty quantification techniques require repeatedly getting outputs from the model for different parameter configurations typically thousands of times. This leads to very large computational times, making this approach practically infeasible in a clinical setting. I address this problem with emulation using Gaussian Processes (GPs), by developing a computationally tractable statistical surrogate model of the original intractable mathematical or physical model (the “digital twin”). In this talk I will present the application of GP emulation to two cardiovascular models: one of the pulmonary circulation, and a second of the coronary circulation.

TBC

Abstract