The Henry M. Jackson Foundation for the Advancement of Military Medicine

Advancing Military Medicine

Search Results - seth+schobel-mchugh

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Prediction of Venous Thromboembolism Utilizing Machine Learning Models - (HJF 477-17)
Background Individuals exposed to physical trauma, including battlefield injuries and major accidents, have an elevated risk of developing venous thromboembolism (VTE). Existing diagnostic approaches can be difficult to apply in acute trauma settings because robust and invasive diagnostic methods may not be feasible in the field or at the bedside....
Published: 7/16/2026   |   Updated: 5/28/2026   |   Inventor(s): Eric Elster, John Oh, Beverly Gaucher, Seth Schobel-McHugh
Keywords(s): Biomarker, Clinical Decision Support Systems, Machine Learning, Predictive Modeling, Trauma, Venous Thromboembolism
Category(s): Diagnostic
Use of Machine Learning Models for Prediction of Clinical Outcomes (HJF 529-18)
Background Individuals exposed to physical trauma, including battlefield injuries and major accidents, are at elevated risk of severe complications. Existing diagnostic approaches can make it difficult to accurately quantify clinical risk and guide treatment decisions, particularly in environments where advanced diagnostics are unavailable. Early identification...
Published: 7/16/2026   |   Updated: 5/28/2026   |   Inventor(s): Seth Schobel-McHugh, Matthew Bradley
Keywords(s): Biomarker, Critical Care, Diagnostics, Machine Learning, Random Forest Model, Respiratory, Respiratory Distress, Sepsis, Traumatic Brain Injury
Category(s): Diagnostic
System and Method for Predicting Pulmonary and Other Complications Following Rib Fractures - (HJF 690-24)
Introduction Scientists at the Henry M. Jackson Foundation (HJF), the Uniformed Services University of the Health Sciences (USUHS), and Duke University (Duke) have developed innovative systems, methods, and computational models designed to predict clinical complications such as acute lung injury (ALI), acute kidney injury (AKI), pneumonia, and respiratory...
Published: 7/6/2026   |   Updated: 12/19/2025   |   Inventor(s): Joseph Fernandez-Moure, Seth Schobel-McHugh, Scott Grey, Renhua Li, Eric Elster
Keywords(s): Acute Kidney Injury (AKI), Acute Lung Injury (ALI), Biomarker, CDST, Chemokines, Cytokines, Immune Response Dynamics, Injury Trauma Prediction, Machine Learning, Personalized Treatment, Respiratory Distress
Category(s): Diagnostic
Non-invasive Clinical Decision Support Tool to Predict Allograft Rejection (HJF 662-23)
Vascularized Composite Allotransplantation (VCA) is a reconstructive option that involves transplantation of composite tissues, such as skin, muscle, and bone, in one surgical procedure. However, graft rejection remains a major challenge that needs to be addressed to improve patient outcomes. To address this challenge, scientists at Johns Hopkins University...
Published: 7/1/2026   |   Updated: 5/16/2025   |   Inventor(s): Renhua Li, Gerald Brandacher, Eric Elster, Seth Schobel-McHugh, Byoung Chol Oh
Keywords(s): Allograft rejection, Graft Failure, Reconstruction
Category(s): Diagnostic
Predictive Tool on Probability of Successful Wound Closure - (HJF 637-22)
Greater than 20% of traumatic wound closure attempts fail due to the immuno-molecular status of the healing wound tissue. Researchers at HJF and the Uniformed Services University for Health Sciences (USU) developed a product that analyzes the individual's wound healing immune response and computes a risk of failure score based on cytokine levels...
Published: 7/1/2026   |   Updated: 10/6/2023   |   Inventor(s): Seth Schobel-McHugh, Eric Elster, Henry Robertson, Felipe Lisboa, Michael Rouse, Scott Grey
Keywords(s): Biomarker, CDST, Dehisced Wound, Effluent, Healing, Serum, Wound, Wound Closure, Wound Failure
Category(s): Diagnostic