The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Jul. 28, 2020
Filed:
Apr. 08, 2011
Alexander Stojadinovic, Chevy Chase, MD (US);
Eric Elster, Kensington, MD (US);
Doug K. Tadaki, Frederick, MD (US);
John S. Eberhardt, Iii, Washington, DC (US);
Trevor Brown, Washington, DC (US);
Thomas A. Davis, Oak Hill, VA (US);
Jonathan Forsberg, Kensington, MD (US);
Jason Hawksworth, Silver Spring, MD (US);
Alexander Stojadinovic, Chevy Chase, MD (US);
Eric Elster, Kensington, MD (US);
Doug K. Tadaki, Frederick, MD (US);
John S. Eberhardt, III, Washington, DC (US);
Trevor Brown, Washington, DC (US);
Thomas A. Davis, Oak Hill, VA (US);
Jonathan Forsberg, Kensington, MD (US);
Jason Hawksworth, Silver Spring, MD (US);
THE UNITED STATES OF AMERICA AS REPRESENTED BY THE SECRETARY OF THE NAVY, Arlington, VA (US);
THE GOVERNMENT OF THE UNITED STATES, AS REPRESENTED BY THE SECRETARY OF THE ARMY, Fort Detrick, MD (US);
DECISIONQ CORPORATION, Arlington, VA (US);
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.
Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile