Physiological systems are well recognised to be nonlinear, stochastic and complex. In situations when only one time series of a single variable is available, exacting useful information from the dynamic data is crucial to facilitate personalised clinical decisions and deepen the understanding of the underlying mechanisms. This thesis is focused on establishing and validating data-driven models that incorporate nonlinearity and stochasticity into the model developing framework, to describe a single measurement time series in the field of biomedical engineering. The tasks of model selection and parameter estimation are performed by applying the variational Bayesian method, which has shown great potential as a deterministic alternative to Markov Chain Monte Carlo sampling methods. The free energy, a maximised lower bound of the model evidence, is considered as the main model selection criterion, which penalises the complexity of the model. Several other model selection criteria, alongside the free energy criterion, have been utilised according to the specific requirements of each application. The methodology has been employed to two biomedical applications. For the first application, a nonlinear stochastic second order model has been developed to describe the blood glucose response to food intake for people with and without Diabetes Mellitus (DM). It was found that the glucose dynamics for the people with DM show a higher degree of nonlinearity and a different range of parameter values compared with people without DM. The developed model shows clinical potential of classifying individuals into these two groups, monitoring the effectiveness of the diabetes management, and identifying people with pre-diabetes conditions. For the second application, a linear third order model has been established for the first time to describe post-transplant antibody dynamics after high-risk kidney transplantation. The model was found to have different ranges of parameter values between people with and without acute antibody-mediated rejection (AMR) episodes. The findings may facilitate the formation of an accurate pre-transplant risk profile which predicts AMR and allows the clinician to intervene at a much earlier stage, and therefore improve the outcomes of high-risk kidney transplantation.
|Publisher||University of Warwick|
|Source Sets||Ethos UK|
|Type||Electronic Thesis or Dissertation|
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