Machine Learning (ML) Algorithm to Predict Mortality of Trauma Patients Admitted to the Intensive Care Units (ICU) in...

Predicting mortality among trauma patients is a critical task that can guide clinical decision-making, better management and resource allocation in Intensive Care Units (ICU). Machine learning has been increasingly employed in clinical practice to effectively predict the mortality of critically ill patients.

This study aimed to develop and evaluate the machine learning models for predicting the mortality of trauma patients.

A multicentre cross-sectional study was conducted. The data were collected retrospectively from 613 trauma patients admitted to the ICU between January 1, 2020, and December 30, 2021, in comprehensive specialised hospitals of Northwest Ethiopia. The Kampala trauma score (KTS II) and revised trauma score (RTS) were calculated for each patient on admission, and the scores range from 5 to 10 and 0 to 7.84, respectively, with lower scores indicating more severe trauma and a higher risk of mortality. Pre-processing, feature selection and model fitting were done using Python version 3.12. Seven Machine Learning (ML) models, Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), K-Nearest Neighbours (KNN), Logistic Regression (LR), Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost), were developed to predict mortality among trauma patients at the time of hospital discharge. The dataset was divided into training (80%) and testing sets (20%), and a 10-fold cross-validation technique was employed to improve model performance. The models' prediction accuracy was measured using metrics derived from the confusion matrix, such as sensitivity, specificity, precision and Receiver Operating Characteristics (ROC).

Of 613 trauma patients admitted to the intensive care units, 248 (40.5%) died. Among the different variables included, the Kampala trauma score (KTS II), Glasgow Coma Scale (GCS) score and presence of complications were the most reliable features for predicting mortality among trauma patients. This study found that the Random Forest (RF) algorithm outperformed other machine learning algorithms, achieving an accuracy of 95%, sensitivity of 96%, precision of 93%, F1 score of 94% and a Receiver Operating Characteristics (ROC) score of 99%. Moreover, Support Vector Machines (SVM) and XGBoost also performed exceptionally well, with AUC scores of 0.98 and 0.97, respectively.

We found the Random Forest (RF) to be the best-performing machine learning model in predicting the mortality among trauma patients. This is the first machine learning model developed specifically for mortality prediction of trauma patients in Ethiopia. The application of machine learning algorithms is warranted to stratify the risk of mortality, enabling evidence-based intervention and maximising resource utilisation. Thus, further external validation on independent data from prospective studies is needed to evaluate the universal applicability of the model to clinical practice.

This study holds substantial value for clinical practice by enhancing decision support, enabling early identification of high-risk patients and supporting proactive surveillance and timely interventions. The integration of machine learning for mortality prediction is particularly impactful, as it facilitates remote monitoring and telemedicine, helping to bridge gaps in healthcare access. Additionally, it aids in optimising treatment strategies through patient-centred data, informs health planning and resource allocation, supports personalised care and advances data-driven research and policy-making.