Bergman’s Minimal Model (MM) captures simply but accurately the homeostasis of glucose and insulin in plasma. The MM has been proposed as an estimator of insulin for Diabetes Mellitus. Along this line of research, the present work takes into account the error between Bergman’s simple compartmental model and the complex physiologic system it depicts. The author employs a Particle Filter (PF) in order to construct the posterior probability density of insulin from data. As a sequential Bayesian estimator, the PF can handle nonlinear state equations, such as the MM, as well as non-Gaussian modeling error. These advantages of the PF over the Kalman Filter warrant further consideration for insulin estimation and, in turn, avoidance of hyperinsulinemia.

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