Could machine learning algorithms help us predict massive bleeding at prehospital level?

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.

[Updates on severe traumatic brain injury management].

Traumatic brain injury (TBI) is an important reason of morbidity-mortality all over the world, affecting young males more and generating Public Health problem. Unfortunately, the advances in the pathophysiology knowledge have not followed a similar development in therapeutic options, there currently not being any contrasted neuroprotectants. In this article, we have reviewed the epidemiology, pathophysiology and therapeutic measures used in the management of patient with severe TBI.