Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Mar 2, 2026

Systems Analysis of the Neuroinflammatory and Hemodynamic Response to Traumatic Brain Injury

Systems Analysis of the Neuroinflammatory and Hemodynamic Response to Traumatic Brain Injury

Published on: May 27, 2022

Machine Learning for Predicting Outcomes in Trauma.

Nehemiah T Liu¹, Jose Salinas

¹US Army Institute of Surgical Research, JBSA Fort Sam Houston, Texas.

Shock (Augusta, Ga.)

|May 13, 2017

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

AI Diffusion Models Generate Realistic Synthetic Dental Radiographs Using a Limited Dataset.

Journal of imaging·2025

Same author

Experience with second allogeneic hematopoietic stem cell transplantation in Chilean patients: A single-center study.

Hematology, transfusion and cell therapy·2025

Same author

Field Testing of the Compensatory Reserve Measurement Monitoring Capability: Results From the 2024 Army Warfighter Expeditionary Experiment.

Military medicine·2025

Same author

Acute myeloid leukemia: a comprehensive update.

Current opinion in hematology·2025

Same author

Advanced decision support for monitoring casualties with hemorrhage: Evidence against the sole reliance on standard vital signs.

The journal of trauma and acute care surgery·2025

Same author

Decrease in Total Leukocyte Count is Associated with Acute Kidney Injury After Severe Burn.

Journal of burn care & research : official publication of the American Burn Association·2025

Same journal

Diagnostic value of miR-126-5p in sepsis-induced acute kidney injury and its molecular mechanism via CASP3.

Shock (Augusta, Ga.)·2026

Same journal

Temporal changes of the von Willebrand factor-ADAMTS13 axis during the first 24 hours in major trauma patients with isolated brain injury and without brain injury: a prospective observational study.

Shock (Augusta, Ga.)·2026

Same journal

Epinephrine nebulization delays need for life-saving intervention following smoke inhalation in ovine model.

Shock (Augusta, Ga.)·2026

Same journal

Sodium-Glucose Cotransporter 2 Inhibitors as Discharge Medications in Survivors of Acute Myocardial Infarction Complicated by Cardiogenic Shock.

Shock (Augusta, Ga.)·2026

Same journal

Dynamic Temperature Modeling Predicts Mortality in Murine Sepsis.

Shock (Augusta, Ga.)·2026

Same journal

Changes of Intestinal Aquaporins after Experimental Polytrauma and Hemorrhagic Shock: Erratum.

Shock (Augusta, Ga.)·2026

See all related articles

Machine learning (ML) shows promise for predicting trauma outcomes, but performance varies widely. Further validation and standardized criteria are needed for widespread clinical adoption of these predictive models.

Area of Science:

Medical Informatics
Computational Biology
Trauma Surgery

Background:

Machine learning (ML) applications in trauma outcome prediction are emerging.
No comprehensive reviews currently exist on ML for trauma outcome prediction.
Understanding ML model performance and common features is crucial for trauma patient assessment and triage.

Purpose of the Study:

To systematically review and identify studies utilizing ML for predicting trauma outcomes.
To assess the commonalities in ML models predicting similar trauma outcomes.
To evaluate the performance variability of ML models in trauma prediction.

Main Methods:

A comprehensive search of MEDLINE and other databases was conducted for studies on trauma and ML.
Inclusion criteria focused on observational studies predicting trauma outcomes.

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Related Experiment Videos

Last Updated: Mar 2, 2026

Systems Analysis of the Neuroinflammatory and Hemodynamic Response to Traumatic Brain Injury

Systems Analysis of the Neuroinflammatory and Hemodynamic Response to Traumatic Brain Injury

Published on: May 27, 2022

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Data extraction included outcome measures, ML algorithms used, and performance metrics.

Main Results:

Sixty-five observational studies, including 2,433,180 patients, met the inclusion criteria.
Common prediction outcomes included survival/mortality, morbidity, and hospital length of stay.
While most studies showed ML benefits, algorithm performance varied significantly, with sensitivity-specificity gap values ranging from 0.035 to 0.927.

Conclusions:

ML models share common features for predicting trauma outcomes, suggesting a potential standardized feature base.
Significant variability in ML algorithm performance necessitates further research.
Widespread adoption of ML in trauma care requires prospective validation, standardized performance criteria, and evidence of clinical and economic impact.