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CLINICAL DECISION MODEL
An embodiment of the invention provides a method for determining a patient-specific probability of transplant glomerulopathy. The method collects clinical parameters from a plurality of patients to create a training database. A fully unsupervised Bayesian Belief Network model is created using data from the training database; and, the fully unsupervised Bayesian Belief Network is validated. Clinical parameters are collected from an individual patient; and, such clinical parameters are input into the fully unsupervised Bayesian Belief Network model via a graphical user interface. The patient-specific probability of transplant glomerulopathy is output from the fully unsupervised Bayesian Belief Network model and sent to the graphical user interface for use by a clinician in pre-operative planning. The fully unsupervised Bayesian Belief Network model is updated using the clinical parameters from the individual patient and the patient-specific probability of transplant glomerulopathy.
Docket:WRAMC 2009-04
Publication/Issued No.:8,510,245
Publication/Issue Date:2013-08-13
Categories: Method
More Detail:Visit USPTO.GOV
Lab:WRAMC
Inventor(s):STOJADINOVIC, A.; EBERHARDT, J.; ELSTER, E.; PEOPLES, G. NISSAN, A.