ROSLYN BERNSTEIN MANNON, M.D.
Osteopathic Medicine at University Blvd, Birmingham, AL

2011029, Dec 1, 2011

Oct 15, 2009

13/123406

Alexander Stojadinovic - Chevy Chase MD, US
Eric A. Elster - Kensington MD, US
Doug K. Tadaki - Frederick MD, US
Trevor S. Brown - Washington DC, US
Thomas A. Davis - Oak Hill VA, US
Jonathan Forsberg - Kensington MD, US
Jason Hawksworth - Silver Spring MD, US
Roslyn Mannon - Birmingham AL, US
Aviram Nissan - Aviram-Yehuda, IL

G06F 15/18

706 12

An embodiment of the invention provides a method for determining a patient-specific probability of disease. 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 disease 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 disease.

2011028, Nov 24, 2011

Apr 8, 2011

13/083184

Alexander Stojadinovic - Chevy Chase MD, US
Eric A. Elster - Kensington MD, US
Doug K. Tadaki - Frederick MD, US
Trevor Brown - Washington DC, US
Thomas A. Davis - Oak Hill VA, US
Jonathan Forsberg - Kensington MD, US
Jason Hawksworth - Silver Spring MD, US
Roslyn Mannon - Birmingham AL, US

G06N 5/00, G06F 17/00

706 45

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.