Complex physiological events such as hemorrhage are met with a continuum of responses in individual test subjects that range from complete compensation to circulatory failure. Predicting the circulatory outcome of an individual potentially affects treatment modalities, for example, by indicating that aggressive intervention is justified based on the likelihood of a negative result with a more passive therapy. We have previously determined an algorithm for calibrating and sampling parameter distributions that generate experimentally verified output distributions via an application of the Metropolis algorithm. This technique is advanced here by the addition of a three-pronged post hoc analysis. First is an inductive algorithm generating minimal parameter sets yielding efficient classification (MER). This algorithm is validated with PCA on the resulting parameter subsets. Finally, we provide an analysis on the response characteristics of clusters determined by a density dependent algorithm on the parameter/variable subspace indicated by the MER.

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