Strategies to Make Models Useful

Using computational models to address clinical questions has become common in biomedical engineering field. For example, one may find in the literature a large library of ODE systems describing everything from sub-cellular to whole-body cardiovascular function. However, constructing such models is only half the story. For them to be truly useful in clinical settings, we must be able to tailor them to a specific patient or a condition easily. As the model complexity and the number of equations grew, paradoxically we have moved further away from achieving this goal.

This leaves us in a position where personalised models are difficult to apply to large patient population and the ability to do so would open up new horizons for mathematical modelling. In order to begin working toward larger patient populations, two important objectives must be met: rapid personalisation (fitting), and robustness of the fitted solution in the presence of noise. Novel methodologies developed in different fields have the potential to reduce manual work, improve fitting quality and harness sensitivity to measurement noise. However, these techniques have not yet become the standard in cardiac applications.

" What exactly is the goal ? "

 The aim of this project is to develop a reliable automatic personalisation strategy for a lumped parameter cardiovascular model, which has been developed for patient-specific applications. The performance of the method will be tested based on its ability to reproduce pre- and post- data from patients undergoing surgical procedures.

"How exactly will we achieve this goal ? "

We will begin by extracting a suitable sub-component of the full model and assessing fitting methods for problems in which the ground truth is known. Various methods ranging from gradient-based techniques to sequential filtering and Monte-Carlo methods will be investigated. The algorithm's performance will be judged according to their automatic capabilities, ability to recreate the clinical data and handle noisy data. The complexity of the model will then be increased, and the results of the initial algorithm ranking adjusted if necessary.

"What experience will I gain in this project ? "

By working on this project, you will gain experience in algorithms, algorithm comparison, handling and integrating clinical data and general computational implementation. This experience will be valuable in any further academic or industry work. You'll mainly be working in a Matlab environment so experience with this will be useful.

" Is this an isolated project or am I part of something bigger? "

Something bigger ! This project is useful to an ongoing research project at King's investigating a large patient population (200+ patients)  and response to Cardiac Resynchronisation Therapy. This therapy, think pacemakers, has a response rate of 2 in 3 and its currently unknown why some patients don't get better. At King's, clinicians and basic scientists are trying to investigate this question through mathematical modelling and clinical imaging.