USA As Represented by Secretary of the Navy

US Government As Represented by the Secretary of the Army

Decisionq Corporation

Eric Elster

Thomas A Davis

Doug K Tadaki

Trevor S Brown

Jonathan A Forsberg

John S Eberhardt, Iii

Alexander Stojadinovic

Jason Hawksworth

Roslyn Bernstein Mannon

Rahul Jindal

Khairul Anam

Douglas K Tadaki

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.

Clinical decision model

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.

Bayesian modeling of pre-transplant variables accurately predicts kidney graft survival

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.

Allograft tolerance induction

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.

Bayesian clinical decision model for determining probability of transplant glomerulopathy

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Kensington, MD, United States of America

Eric Elster