Improving sepsis mortality prediction with machine learning: A comparative study of advanced classifiers and performance metrics

High sepsis mortality rates pose a serious global health problem. Machine learning is a promising technique with the potential to improve mortality prediction for this disease in an accurate and timely manner. This study aimed to develop a model capable of rapidly and accurately predicting sepsis mortality using data that can be quickly obtained in an ambulance, with a focus on practical application during ambulance transport. Data from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) dataset were used to compare the performance of 11 machine learning algorithms against the widely utilized quick Sequential Organ Failure Assessment (qSOFA) score. A dynamic updating model was implemented. Performance was evaluated using area under the curve (AUC) and precision-recall area under the curve (PRAUC) scores, and feature importance was assessed with SHapley Additive exPlanations (SHAP) values. The light gradient boosting machine (LightGBM) model achieved the highest AUC (0.79) and PRAUC (0.44) scores, outperforming the qSOFA score (AUC = 0.76, PRAUC = 0.40). The LightGBM also achieved the highest PRAUC (0.44), followed by Optuna_LightGBM (0.43) and random forest (0.42). The dynamically updated and tuned model further improved performance metrics (AUC = 0.79, PRAUC = 0.44) compared to the base model (AUC = 0.76, PRAUC = 0.39). Feature importance analysis offers clinicians insights for prioritizing patient assessments and interventions. The LightGBM-based model demonstrated superior performance in predicting sepsis-related mortality in an ambulance setting. This study underscores the practical applicability of machine learning models, addressing the limitations of previous research, and highlights the importance of real-time updates and hyperparameter tuning in optimizing model performance.