Machine Learning-Based Prediction of Mortality in Geriatric Traumatic Brain Injury Patients
Journal:
arXiv
Published Date:
May 19, 2025
Abstract
Traumatic Brain Injury (TBI) is a major contributor to mortality among older
adults, with geriatric patients facing disproportionately high risk due to
age-related physiological vulnerability and comorbidities. Early and accurate
prediction of mortality is essential for guiding clinical decision-making and
optimizing ICU resource allocation. In this study, we utilized the MIMIC-III
database to identify geriatric TBI patients and applied a machine learning
framework to develop a 30-day mortality prediction model. A rigorous
preprocessing pipeline-including Random Forest-based imputation, feature
engineering, and hybrid selection-was implemented to refine predictors from 69
to 9 clinically meaningful variables. CatBoost emerged as the top-performing
model, achieving an AUROC of 0.867 (95% CI: 0.809-0.922), surpassing
traditional scoring systems. SHAP analysis confirmed the importance of GCS
score, oxygen saturation, and prothrombin time as dominant predictors. These
findings highlight the value of interpretable machine learning tools for early
mortality risk stratification in elderly TBI patients and provide a foundation
for future clinical integration to support high-stakes decision-making in
critical care.
