Clinical decision model

US10726943B2 · kind B2 · utility

Assignees

• United States of America as represented by the Secretary of the Air Force · US
• The Government of the United States, as represented by the Secretary of the Army · US
• DECISIONQ CORPORATION · US

Inventors

• Alexander Stojadinovic · Bethesda, US
• Eric A. Elster · Kensington, US
• Doug K. Tadaki · Frederick, US
• John S. Eberhardt, III · Jeffersonton, US
• Trevor Brown · Washington, US
• Thomas A. Davis · Centreville, US
• Jonathan Forsberg · Kensington, US
• Jason Hawksworth · Silver Spring, US

Key dates

• Filing date: Apr 8, 2011
• Grant date: Jul 28, 2020
• Expiry date: Oct 29, 2034

Classification

• Technology area (CPC G): Physics
• CPC primary: G16B50/00
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

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.