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Updated: Jul 21, 2025
A New Hybrid Quantitative Evaluation Model for Axillary Junctional Hemorrhage in Swine
Published on: December 6, 2024
Could machine learning algorithms help us predict massive bleeding at prehospital level?
Marcos Valiente Fernández1, Carlos García Fuentes1, Francisco de Paula Delgado Moya1
1Hospital Universitario 12 de Octubre, UCI de Trauma y Emergencias, Madrid. Spain.
Objective:
Comparison of the predictive ability of various machine learning algorithms (MLA) versus traditional prediction scales (TPS) for massive hemorrhage (MH) in patients with severe traumatic injury (STI).
Design:
On a database of a retrospective cohort with prehospital clinical variables and MH outcome, a treatment of the database was performed to be able to apply the different AML, obtaining a total set of 473 patients (80% training, 20% validation). For modeling, proportional imputation and cross validation were performed. The predictive power was evaluated with the ROC metric and the importance of the variables using the Shapley values.
Setting:
Out-of-hospital care of patients with STI.
Participants:
Patients with STI treated out-of-hospital by a out-of-hospital medical service from January 2010 to December 2015 and transferred to a trauma center in Madrid.
Interventions:
None.
Main Variables Of Interest:
Obtaining and comparing the "Receiver Operating Characteristic curve" (ROC curve) metric of four MLAs: "random forest" (RF), "vector support machine" (SVM), "gradient boosting machine" (GBM) and "neural network" (NN) with the results obtained with TPS.
Results:
The different AML reached ROC values higher than 0.85, having medians close to 0.98. We found no significant differences between AMLs. Each AML offers a different set of more important variables with a predominance of hemodynamic, resuscitation variables and neurological impairment.
Conclusions:
MLA may be helpful in patients with HM by outperforming TPS.
