Bayesian modelling of vital signs in critically ill patients

EPSRC areas under which the project falls:
Operational Research
Statistics and applied probability

This project will be part of the EPSRC-funded CHIMERA mathematical science in healthcare hub. CHIMERA aims to develop new mathematical models and data science tools which utilise the wealth of physiological data collected for each patient to provide clinicians with a better idea of how well the patient's body is recovering since their admission to an intensive care unit. This project will determine the relevant model parameters. It will also explore various contender models that simulate the mechanical responses and interactions between respiratory and cardiovascular systems and use advanced numerical and statistical techniques to optimise the model structure and fit the parameters to the available clinical data. For the best performing models, large cohorts of virtual subjects will be produced by varying the values of clinically relevant parameters with the purpose of classifying patients into different risk categories of a certain event. Bayesian hierarchical models will be implemented for each candidate model given that the classification decision boundaries from model parameters are likely to be non-linear and not simply connected. Our clinical partners will be explicitly consulted to see how/which of the risk classifications obtained might best assist clinical decision making.