Trevor R Brown

US Patent:

8510245, Aug 13, 2013

Filed:

Apr 8, 2011

Appl. No.:

13/083184

Inventors:

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

Assignee:

The United States of America as Represented by the Secretary of the Army - Washington DC

International Classification:

G06N 5/00

US Classification:

706 45

Abstract:

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.

US Patent:

20110289035, Nov 24, 2011

Filed:

Apr 8, 2011

Appl. No.:

13/083090

Inventors:

Alexander Stojadinovic - Chevy Chase MD, US
Eric 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

International Classification:

G06N 5/00
G06F 17/00

US Classification:

706 45

Abstract:

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 the healing rate of an acute traumatic wound 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 the healing rate of an acute traumatic wound.

US Patent:

20110295782, Dec 1, 2011

Filed:

Oct 15, 2009

Appl. No.:

13/123406

Inventors:

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

International Classification:

G06F 15/18

US Classification:

706 12

Abstract:

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.

US Patent:

20200335179, Oct 22, 2020

Filed:

Jul 7, 2020

Appl. No.:

16/923014

Inventors:

- Arlington VA, US
- Fort Detrick 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

International Classification:

G16B 40/00

Abstract:

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 the healing rate of an acute traumatic wound 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 the healing rate of an acute traumatic wound.

US Patent:

20160206249, Jul 21, 2016

Filed:

Oct 27, 2012

Appl. No.:

13/662456

Inventors:

Eric A. Elster - Kensington MD, US
Doug Tadaki - Frederick MD, US
Trevor S. Brown - Washington DC, US
Rahul Jindal - Silver Spring MD, US

International Classification:

A61B 5/00

Abstract:

An embodiment of the invention provides a method for determining a patient-specific probability of renal transplant survival. The method collects clinical parameters from a plurality of renal transplant donor and patient 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/donor; 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 organ matching. The fully unsupervised Bayesian Belief Network model is updated using the clinical parameters from the individual patient and the patient-specific probability of transplant survival.