Machine Learning-Based Prediction of Mortality in Geriatric Traumatic Brain Injury Patients

TL;DR

This study develops a machine learning model using clinical data to accurately predict 30-day mortality in elderly traumatic brain injury patients, aiding clinical decision-making.

Contribution

It introduces a refined predictive framework with feature engineering and hybrid selection, achieving high accuracy and interpretability over traditional methods.

Findings

CatBoost model achieved AUROC of 0.867

Key predictors include GCS score, oxygen saturation, and prothrombin time

Model outperforms traditional scoring systems

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.