Application of machine learning to predict and identify factors associated with the need for surgery in traumatic epidural hematoma.

In this study, the novel integration of machine learning with a nomogram offers clinicians a precise, user-friendly tool for rapid decision-making, potentially reducing complications. These findings help surgeons to make more informed clinical decisions by accurately assessing these parameters in the early stages and to identify patients at higher risk for surgical intervention more quickly.