1. A method for determining a patient-specific probability of renal transplant survival, said method including: a) collecting clinical parameters from a plurality of patients and donor to create a training database, the clinical parameters; b) creating a fully unsupervised Bayesian Belief Network model using data from the training database; c) validating the fully unsupervised Bayesian Belief Network model; d) collecting the clinical parameters for an individual patient and an donor; e) receiving the clinical parameters for the individual patient and an donor into the fully unsupervised Bayesian Belief Network model; f) outputting the patient-specific probability of transplant survival from the fully unsupervised Bayesian Belief Network model to a graphical user interface for use by a clinician; and g) updating the fully unsupervised Bayesian Belief Network model using the clinical parameters for the individual patient and for the donor, and the patient-specific probability of transplant survival.