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

Advancing Military Medicine

Search Results - machine+learning

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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
Rapid, Point-of-care Sepsis Mortality Prediction System – (HJF 655-23)
Researchers at the Henry M. Jackson Foundation for the Advancement of Military Medicine (HJF) have developed systems and methods for predicting sepsis mortality risk using biomarker data and clinical parameters. Applications and Advantages Enables early and accurate prediction of sepsis mortality risk. Supports improved clinical decision-making...
Published: 6/22/2026   |   Updated: 5/21/2026   |   Inventor(s): Danielle Clark, Joost Brandsma, Joshua Chenoweth, Deborah Striegel
Keywords(s): Clinical Decision Support Systems, Critical Care, Diagnostics, Gene Expression Assays, Machine Learning, Point of Care, Sepsis
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