Patents by Inventor Eric A. Elster
Eric A. Elster 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).
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Publication number: 20230019900Abstract: The present disclosure describes methods and systems for predicting if a subject has an increased risk of having or developing venous thromboembolism, including prior to the detection of symptoms thereof and/or prior to onset of any detectable symptoms thereof. The present disclosure also describes a method of generating a model for predicting venous thromboembolism.Type: ApplicationFiled: December 3, 2020Publication date: January 19, 2023Applicants: Henry M. Jackson Foundation for the Advancement of Military Medicine, The Government of the United States, as represented by the Secretary of the Army, The United States of America, as Represented by the Secretary of the NavyInventors: Matthew J. Bradley, Eric A Elster, Vivek Khatri, John S Oh, Seth A Schobel
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Publication number: 20210327540Abstract: The present disclosure describes methods and systems for predicting if a subject has an increased risk of having or developing one or more clinical outcomes, including prior to the detection of symptoms thereof and/or prior to onset of any detectable symptoms thereof. The present disclosure also describes a method of generating a model for predicting one or more clinical outcomes.Type: ApplicationFiled: August 16, 2019Publication date: October 21, 2021Applicants: Henry M. Jackson Foundation for the Advancement of Military Medicine, Naval Medical Research Center, Duke University Medical Center, Emory UniversityInventors: Seth A Schobel, Vivek Khatri, Felipe Lisboa, Matthew J. Bradley, Christopher J. Dente, Timothy Buchman, Allan D. Kirk, Jonathan A. Forsberg, Todd V. Brennan, Eric A. Elster
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Publication number: 20200335179Abstract: 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: ApplicationFiled: July 7, 2020Publication date: October 22, 2020Inventors: Alexander Stojadinovic, Eric A. Elster, Doug K. Tadaki, John S. Eberhardt, III, Trevor Brown, Thomas A. Davis, Jonathan Forsberg, Jason Hawksworth
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Patent number: 10726943Abstract: 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: GrantFiled: April 8, 2011Date of Patent: July 28, 2020Assignees: 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 CORPORATIONInventors: Alexander Stojadinovic, Eric Elster, Doug K. Tadaki, John S. Eberhardt, III, Trevor Brown, Thomas A. Davis, Jonathan Forsberg, Jason Hawksworth
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Publication number: 20190355473Abstract: Described herein are systems and methods for determining if a subject has an increased risk of having or developing pneumonia or symptoms associated with pneumonia. Also described are systems and methods for predicting a pneumonia outcome for a subject, systems and methods for generating a model for predicting a pneumonia outcome in a subject, systems and method for determining a subject's risk profile for pneumonia, method of determining that a subject has an increased risk of developing pneumonia, and methods of treating a subject determined to have an elevated risk of developing pneumonia, methods of detecting panels of biomarkers in a subject, and methods of assessing risk factors in a subject having an injury, as well as related devices and kits.Type: ApplicationFiled: January 5, 2018Publication date: November 21, 2019Applicant: Henry M. Jackson Foundation for the Advancement of Military MedicineInventors: Seth A. Schobel, Eric A. Elster, Beverly J. Gaucher
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Publication number: 20190354814Abstract: Described herein are systems and methods for determining if a subject has an increased risk of having or developing bacteremia or symptoms associated with bacteremia. Also described are systems and methods for predicting a bacteremia outcome for a subject, systems and methods for generating a model for predicting a bacteremia outcome in a subject, systems and method for determining a subject's risk profile for bacteremia, method of determining that a subject has an increased risk of developing bacteremia, and methods of treating a subject determined to have an elevated risk of developing bacteremia, methods of detecting panels of biomarkers in a subject, and methods of assessing risk factors in a subject having an injury, as well as related devices and kits.Type: ApplicationFiled: January 5, 2018Publication date: November 21, 2019Applicant: Henry M. Jackson Foundation for the Advancement of Military MedicineInventors: Seth A. Schobel, Eric A. Elster, Beverly J. Gaucher
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Patent number: 9561006Abstract: 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.Type: GrantFiled: October 27, 2012Date of Patent: February 7, 2017Assignee: The United States of America as represented by the Secretary of the NavyInventors: Eric A. Elster, Doug Tadaki, Trevor S. Brown, Rahul Jindal
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Publication number: 20160353994Abstract: A method for detecting and monitoring the progression of heterotopic ossification by Raman spectral analysis. Analysis of heterotopic ossification progress can be conducted using invasive or invasive means using specific Raman spectroscopy. Analysis is by determination of a number of Raman spectral parameters including the area under one or more of the vibrational bands, band area ratios, band height, ratios of band heights and shift in band center.Type: ApplicationFiled: January 2, 2014Publication date: December 8, 2016Inventors: Nicole Crane, Eric A. Elster, Jonathan Forsberg, Douglas Tadaki
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Patent number: 9457052Abstract: A method inducing chimerism and allograft tolerance by co-infusion of stem/progenitor-like cells and donor cells, wherein the donor cells can be bone marrow cells. The method also comprises the conditioning comprising depletion of CD4+ and CD8+ T-cells and administration of low doses of anti-neoplastic drugs. The inventive method comprises an aspect wherein allograft tolerance is induced without systemically suppressing the immune system.Type: GrantFiled: March 14, 2014Date of Patent: October 4, 2016Assignee: The United States of America as Represented by the Secretary of the NavyInventors: Thomas A. Davis, Khairul Anam, Eric A. Elster, Douglas K. Tadaki
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Publication number: 20160256489Abstract: A method inducing chimerism and allograft tolerance by co-infusion of stein/progenitor-like cells and donor cells, wherein the donor cells can be bone marrow cells. The method also comprises the conditioning comprising depletion of CD4+ and CD8+ T-cells and administration of low doses of anti-neoplastic drugs. The inventive method comprises an aspect wherein allograft tolerance is induced without systemically suppressing the immune system.Type: ApplicationFiled: March 14, 2014Publication date: September 8, 2016Inventors: Thomas A. Davis, Khairul Anam, Eric A. Elster, Douglas K. Tadaki
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Publication number: 20160206249Abstract: 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.Type: ApplicationFiled: October 27, 2012Publication date: July 21, 2016Inventors: Eric A. Elster, Doug Tadaki, Trevor S. Brown, Rahul Jindal
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Publication number: 20150258144Abstract: A method inducing chimerism and allograft tolerance by co-infusion of stein/progenitor-like cells and donor cells, wherein the donor cells can be bone marrow cells. The method also comprises the conditioning comprising depletion of CD4+ and CD8+ T-cells and administration of low doses of anti-neoplastic drugs. The inventive method comprises an aspect wherein allograft tolerance is induced without systemically suppressing the immune system.Type: ApplicationFiled: March 14, 2014Publication date: September 17, 2015Inventors: Thomas A. Davis, Khairul Anam, Eric A. Elster, Douglas K. Tadaki
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Publication number: 20150182119Abstract: A method for detecting and monitoring the progression of heterotopic ossification by Raman spectral analysis. Analysis of heterotopic ossification progress can be conducted using invasive or invasive means using specific Raman spectroscopy. Analysis is by determination of a number of Raman spectral parameters including the area under one or more of the vibrational bands, band area ratios, band height, ratios of band heights and shift in band center.Type: ApplicationFiled: January 2, 2014Publication date: July 2, 2015Inventors: Nicole Crane, Eric A. Elster, Jonathan Forsberg, Douglas Tadaki
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Publication number: 20140122382Abstract: 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.Type: ApplicationFiled: October 27, 2012Publication date: May 1, 2014Inventors: Eric A. Elster, Doug Tadaki, Trevor S. Brown, Rahul Jindal
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Patent number: 8510245Abstract: 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: GrantFiled: April 8, 2011Date of Patent: August 13, 2013Assignee: The United States of America as Represented by the Secretary of the ArmyInventors: Alexander Stojadinovic, Eric A. Elster, Doug K. Tadaki, John S. Eberhardt, III, Trevor Brown, Thomas A. Davis, Jonathan Forsberg, Jason Hawksworth, Roslyn Mannon
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Publication number: 20120076776Abstract: 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: ApplicationFiled: February 4, 2011Publication date: March 29, 2012Inventors: ERIC ELSTER, DOUG TADAKI, JASON HAWKSWORTH, THOMAS DAVIS
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Publication number: 20110295782Abstract: 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: ApplicationFiled: October 15, 2009Publication date: December 1, 2011Inventors: 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
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Publication number: 20110289036Abstract: 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: ApplicationFiled: April 8, 2011Publication date: November 24, 2011Inventors: Alexander Stojadinovic, Eric A. Elster, Doug K. Tadaki, John S. Eberhardt, III, Trevor Brown, Thomas A. Davis, Jonathan Forsberg, Jason Hawksworth, Roslyn Mannon
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Publication number: 20110289035Abstract: 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: ApplicationFiled: April 8, 2011Publication date: November 24, 2011Inventors: Alexander Stojadinovic, Eric Elster, Doug K. Tadaki, John S. Eberhardt, III, Trevor Brown, Thomas A. Davis, Jonathan Forsberg, Jason Hawksworth
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Publication number: 20100182415Abstract: A system and method for real-time or near real-time monitoring of tissue/organ oxygenation through visual assessment of contrast enhanced images of the target area of tissue or organ. Video of a target tissue/organ was acquired during surgery, selected image frames were extracted. Each extracted image is separated into red, green and blue CCD responses. A modified contrast image was created by subtracting blue CCD responses from red CCD responses, and plotting the resultant image using a modified colormap. Overlaying said modified contrast image onto the original extracted image frame under a selected transparency range, and display it for review.Type: ApplicationFiled: December 9, 2009Publication date: July 22, 2010Inventors: Eric A. Elster, Doug K. Tadaki, Nicole J. Crane, Scott W. Huffman, Ira W. Levin