A method of predicting progression of a disease in a patient includes selecting a physiological parameter of interest and a range of inputs for a set of individual predictive disease models; running, using a processor, the set of individual predictive disease models with the range of inputs to obtain an estimate from model; identifying experimental observations; identifying critical parameters among the estimates of the physiological parameters of interest, the critical parameters exhibiting a specified correlation with an error in estimation of the physiological parameters of interest; obtaining, for each subspace of all possible combinations of critical parameters, a model based on blending the estimates so that the blended prediction best fits the experimental observations; and determining a prediction to predict disease progression or response to a treatment for the patient using the blended model.
