Abstract
This paper presents the development of virtual patients to enable the simulation evaluation and assessment of multivariable control algorithms for biomedical systems. The virtual patients are generated by fitting the parameters of the models to clinical experimental data, followed by the estimation of the multivariate distribution of the actual patient parameters. The estimated multivariate distribution is then incorporated with constraints to ensure the sampling of synthetic virtual patients conforms to the actual patient parameter bounds. The sampled synthetic virtual patients are analyzed through multivariate statistical techniques and data clustering algorithms to prune out virtual subjects with similar characteristics or unrealistic dynamics, yielding a virtual patient population that is diverse and with individually distinct characteristics. The generated virtual patient population is used to evaluate multivariable nonlinear and adaptive control algorithms for insulin dosing in people with Type 1 diabetes.
Published Version
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