Patents by Inventor Jason Hawksworth

Jason Hawksworth has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20200335179
    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.
    Type: Application
    Filed: July 7, 2020
    Publication date: October 22, 2020
    Inventors: Alexander Stojadinovic, Eric A. Elster, Doug K. Tadaki, John S. Eberhardt, III, Trevor Brown, Thomas A. Davis, Jonathan Forsberg, Jason Hawksworth
  • Patent number: 10726943
    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.
    Type: Grant
    Filed: April 8, 2011
    Date of Patent: July 28, 2020
    Assignees: THE UNITED STATES OF AMERICA AS REPRESENTED BY THE SECRETARY OF THE NAVY, THE GOVERNMENT OF THE UNITED STATES, AS REPRESENTED BY THE SECRETARY OF THE ARMY, DECISIONQ CORPORATION
    Inventors: Alexander Stojadinovic, Eric Elster, Doug K. Tadaki, John S. Eberhardt, III, Trevor Brown, Thomas A. Davis, Jonathan Forsberg, Jason Hawksworth
  • Patent number: 8510245
    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.
    Type: Grant
    Filed: April 8, 2011
    Date of Patent: August 13, 2013
    Assignee: The United States of America as Represented by the Secretary of the Army
    Inventors: Alexander Stojadinovic, Eric A. Elster, Doug K. Tadaki, John S. Eberhardt, III, Trevor Brown, Thomas A. Davis, Jonathan Forsberg, Jason Hawksworth, Roslyn Mannon
  • Publication number: 20120076776
    Abstract: A method for preventing inflammation, comprising administering to a subject a lymphocyte sequestrating or depletion agent before the onset of inflammation. A method for treating inflammation caused by an injury or an infection, comprising depleting immune lymphocyte of a subject by administering to said subject a lymphocyte sequestrating or depletion agent during or after said event. A method for preventing or treating abdominal adhesion comprising administering to a subject a lymphocyte sequestering or a lymphocyte depletion agent.
    Type: Application
    Filed: February 4, 2011
    Publication date: March 29, 2012
    Inventors: ERIC ELSTER, DOUG TADAKI, JASON HAWKSWORTH, THOMAS DAVIS
  • Publication number: 20110295782
    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.
    Type: Application
    Filed: October 15, 2009
    Publication date: December 1, 2011
    Inventors: Alexander Stojadinovic, Eric A. Elster, Doug K. Tadaki, John S. Eberhardt, III, Trevor S. Brown, Thomas A. Davis, Jonathan Forsberg, Jason Hawksworth, Roslyn Mannon, Aviram Nissan
  • Publication number: 20110289035
    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.
    Type: Application
    Filed: April 8, 2011
    Publication date: November 24, 2011
    Inventors: Alexander Stojadinovic, Eric Elster, Doug K. Tadaki, John S. Eberhardt, III, Trevor Brown, Thomas A. Davis, Jonathan Forsberg, Jason Hawksworth
  • Publication number: 20110289036
    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.
    Type: Application
    Filed: April 8, 2011
    Publication date: November 24, 2011
    Inventors: Alexander Stojadinovic, Eric A. Elster, Doug K. Tadaki, John S. Eberhardt, III, Trevor Brown, Thomas A. Davis, Jonathan Forsberg, Jason Hawksworth, Roslyn Mannon