Comparison of Mortality Predictive Models of Sepsis Patients Based on Machine Learning

Abstract

Objective: To compare the performance of five machine learning models and SAPS II score in predicting the 30-day mortality amongst patients with sepsis. Methods: The sepsis patient-related data were extracted from the MIMIC-IV database. Clinical features were generated and selected by mutual information and grid search. Logistic regression, Random forest, LightGBM, XGBoost, and other machine learning models were constructed to predict the mortality probability. Five measurements including accuracy, precision, recall, F1 score, and area under curve (AUC) were acquired for model evaluation. An external validation was implemented to avoid conclusion bias. Results: LightGBM outperformed other methods, achieving the highest AUC (0.900), accuracy (0.808), and precision (0.SS9). All machine learning models performed better than SAPS II score (AUC=0.748). LightGBM achieved 0.883 in AUC in the external data validation. Conclusions: The machine learning models are more effective in predicting the 30-day mortality of patients with sepsis than the traditional SAPS II score.