Predicting Sepsis Mortality Using Machine Learning Methods

Background: Sepsis, a life-threatening condition, is the cause of a large number of mortalities worldwide. Accurate prediction of sepsis outcomes is crucial for effective treatment and management. Previous studies have explored machine learning for prognosis but have limitations in feature sets and model interpretability. Methods: This study analyzes intensive care patient outcomes using the MIMIC-IV database, focusing on adult sepsis cases. Employing the latest data extraction tools, such as Google Big Query, and following stringent selection criteria, we selected 38 features in this study. This selection is also informed by a comprehensive literature review and clinical expertise. We used statistical methods to handle the imbalances inherent in healthcare datasets. Our modeling focused on various classification techniques, with a train-test split preferred over cross-validation for its superior performance and computational efficiency. Results: The Random Forest model emerged as the most effective, achieving an AUROC of 0.94 with a confidence interval of 0.01, significantly outperforming other baseline models, and the best result in our literature review. This study not only yields a high-performing model but also provides granular insights into the factors affecting mortality, and demonstrates the value of advanced analytics in critical care. Conclusions: The study shows significant improvement in predicting sepsis outcomes, indicating the potential of machine learning in critical care. By enhancing model accuracy and stability, this research contributes to clinical decision-making, offering a pathway for data-driven approaches to reduce sepsis-induced fatalities.