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. 2023 Aug 5;13(15):2605.
doi: 10.3390/diagnostics13152605.

Machine Learning Approach for the Prediction of In-Hospital Mortality in Traumatic Brain Injury Using Bio-Clinical Markers at Presentation to the Emergency Department

Ahammed Mekkodathil  1 , Ayman El-Menyar  1   2 , Mashhood Naduvilekandy  3 , Sandro Rizoli  4 , Hassan Al-Thani  4
Affiliations

Machine Learning Approach for the Prediction of In-Hospital Mortality in Traumatic Brain Injury Using Bio-Clinical Markers at Presentation to the Emergency Department

Ahammed Mekkodathil et al. Diagnostics (Basel). .

Abstract

Background: Accurate prediction of in-hospital mortality is essential for better management of patients with traumatic brain injury (TBI). Machine learning (ML) algorithms have been shown to be effective in predicting clinical outcomes. This study aimed to identify predictors of in-hospital mortality in TBI patients using ML algorithms.

Materials and method: A retrospective study was performed using data from both the trauma registry and electronic medical records among TBI patients admitted to the Hamad Trauma Center in Qatar between June 2016 and May 2021. Thirteen features were selected for four ML models including a Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XgBoost), to predict the in-hospital mortality.

Results: A dataset of 922 patients was analyzed, of which 78% survived and 22% died. The AUC scores for SVM, LR, XgBoost, and RF models were 0.86, 0.84, 0.85, and 0.86, respectively. XgBoost and RF had good AUC scores but exhibited significant differences in log loss between the training and testing sets (% difference in logloss of 79.5 and 41.8, respectively), indicating overfitting compared to the other models. The feature importance trend across all models indicates that aPTT, INR, ISS, prothrombin time, and lactic acid are the most important features in prediction. Magnesium also displayed significant importance in the prediction of mortality among serum electrolytes.

Conclusions: SVM was found to be the best-performing ML model in predicting the mortality of TBI patients. It had the highest AUC score and did not show overfitting, making it a more reliable model compared to LR, XgBoost, and RF.

Keywords: brain injury; head; machine learning; predictors; support vector machine; trauma.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
An overview of the key steps involved in machine learning approach.
Figure 2
Figure 2
Comparison of performance metrics for different machine learning approaches.
Figure 3
Figure 3
Feature importance in XgBoost model predicting mortality of TBI patients.
Figure 4
Figure 4
Feature importance in Random Forest model predicting mortality of TBI patients.
Figure 5
Figure 5
Feature importance in Linear SVM model predicting mortality of TBI patients.
Figure 6
Figure 6
Feature importance in Logistic Regression model predicting mortality of TBI patients.
See this image and copyright information in PMC

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