Prediction of Mortality in Geriatric Traumatic Brain Injury Patients Using Machine Learning Algorithms

Abstract

Background: The number of geriatric traumatic brain injury (TBI) patients is increasing every year due to the population’s aging in most of the developed countries. Unfortunately, there is no widely recognized tool for specifically evaluating the prognosis of geriatric TBI patients. We designed this study to compare the prognostic value of different machine learning algorithm-based predictive models for geriatric TBI. Methods: TBI patients aged ≥65 from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were eligible for this study. To develop and validate machine learning algorithm-based prognostic models, included patients were divided into a training set and a testing set, with a ratio of 7:3. The predictive value of different machine learning based models was evaluated by calculating the area under the receiver operating characteristic curve, sensitivity, specificity, accuracy and F score. Results: A total of 1123 geriatric TBI patients were included, with a mortality of 24.8%. Non-survivors had higher age (82.2 vs. 80.7, p = 0.010) and lower Glasgow Coma Scale (14 vs. 7, p < 0.001) than survivors. The rate of mechanical ventilation was significantly higher (67.6% vs. 25.9%, p < 0.001) in non-survivors while the rate of neurosurgical operation did not differ between survivors and non-survivors (24.3% vs. 23.0%, p = 0.735). Among different machine learning algorithms, Adaboost (AUC: 0.799) and Random Forest (AUC: 0.795) performed slightly better than the logistic regression (AUC: 0.792) on predicting mortality in geriatric TBI patients in the testing set. Conclusion: Adaboost, Random Forest and logistic regression all performed well in predicting mortality of geriatric TBI patients. Prognostication tools utilizing these algorithms are helpful for physicians to evaluate the risk of poor outcomes in geriatric TBI patients and adopt personalized therapeutic options for them.

Supervised Learning Algorithm Comparison in Discharge Status Prediction of Trauma Patients: Empirical Evaluation

“…Similar to the present study, the random forest algorithm showed the best performance, and Logistic Regression yielded acceptable results. In a study by Wang et al [23], it was reported that prognostication tools utilizing Adaboost, Random Forest, and Logistic Regression algorithms proved beneficial for physicians in assessing the risk of poor outcomes in geriatric patients with TBI and in guiding the selection of personalized therapeutic options.…”

“…Fen et al compared the predictive abilities of twentytwo machine learning models with a logistic regression model, using performance measures such as ROC, AUC, accuracy, F-score, precision, recall, and decision curve analysis [22]. Another research focused on evaluating diagnostic accuracy for traumatic brain injury in elderly patients using various machine learning algorithms [23]. Rau et al predicted patient deaths using logistic regression, support vector machine, decision tree, naive Bayes, and artificial neural network models [24].…”

Development and Validation of an XGBoost-Algorithm-Powered Survival Model for Predicting In-Hospital Mortality Based on 545,388 Isolated Severe Traumatic Brain Injury Patients from the TQIP Database

“…While these tools provide valuable information, on their own they struggle to capture the complexity of the multifaceted nature of TBI, which limits their prognostic ability in terms of accuracy and individualized risk assessment [ 15 ]. This has prompted researchers and clinicians to explore alternative approaches that leverage the power of machine learning algorithms to improve predictive models in the field of TBI [ 17 , 18 , 19 , 20 , 21 , 22 ].…”