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Bayesian clinical decision model for determining probability of transplant glomerulopathy

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AMRMC

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.

Inventors: 
Stojadinovic, Alexander; Elster, Eric A.; Tadaki, Doug K.; Eberhardt III, John S.; Brown, Trevor; Davis, Thomas A.; Forsberg, Jonathan; Hawksworth, Jason; Mannon, Roslyn
Patent Number: 
Technical domain: 
IT and Software
FIle Date: 
2011-04-08
Grant Date: 
2013-08-13
Grant time: 
858 days
Grant time percentile rank: 
15
Claim count percentile rank: 
2
Citations percentile rank: 
1
'Cited by' percentile rank: 
1
Assignee: 
US ARMY