Patents by Inventor John S. Eberhardt, III

John S. Eberhardt, III 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: 9349103
    Abstract: According to one embodiment, in response to a set of data for anomaly detection, a Bayesian belief network (BBN) model is applied to the data set, including for each of a plurality of features of the BBN model, performing an estimate using known observed values associated with remaining features to generate a posterior probability for the corresponding feature. A scoring operation is performed using a predetermined scoring algorithm on posterior probabilities of all of the features to generate a similarity score, wherein the similarity score represents a degree to which a given event represented by the data set is novel relative to historical events represented by the BBN model.
    Type: Grant
    Filed: January 9, 2013
    Date of Patent: May 24, 2016
    Assignee: DECISIONQ CORPORATION
    Inventors: John S. Eberhardt, III, Todd A. Radano, Benjamin E. Peterson
  • 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: 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
  • Publication number: 20110082712
    Abstract: According to one aspect of the invention, health insurance claim data for a first group of individuals is obtained to generate a training corpus, including a training set of claim data and a holdout set of claim data. The first group of individuals represents enrollees of one or more first health insurance plans and the health insurance claim data represents historic insurance claim information for each individual in the first group. A Bayesian belief network (BBN) model is created by training a BBN network based on the training set of claim data using predetermined machine learning algorithms. The BBN model is validated using the holdout set of claim data. The BBN model, when having been successfully validated, is configured to identify at least one of individuals with risk for a disorder and individuals with risk who are most likely to benefit from intervention and treatment for the disorder.
    Type: Application
    Filed: September 30, 2010
    Publication date: April 7, 2011
    Applicant: DecisionQ Corporation
    Inventors: John S. Eberhardt, III, Philip M. Kalina, Todd A. Radano