Predicting Epidural Hematoma Expansion in Traumatic Brain Injury: A Machine Learning Approach.

ABSTRACT: IntroductionTraumatic brain injury (TBI) is a leading cause of disability and mortality worldwide, with epidural hematoma (EDH) being a severe consequence. This study focuses on identifying factors predicting EDH volume changes in TBI patients and developing a machine learning (ML) model to predict EDH expansion.MethodsThe study includes patients with traumatic EDH between 2019 and 2021. Data were gathered from CT scans performed at the time of admission and 6 hours later, and subsequently analyzed. The data was divided into three cohorts: all cases, adults, and pediatrics. To predict EDH volume changes, we used Logistic Regression (LR), Random Forest (RF), XGBoost, and K-Nearest Neighbors (KNN) models. Data was divided into an 80% training set and a 20% test set. Through a rigorous proces

SUBMITTER: Hasanpour M 

PROVIDER: S-EPMC11586933 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature