Prediction of mortality in sepsis patients using stacked ensemble machine learning algorithm

Introduction: Machine learning (ML) has been tried in predicting outcomes following sepsis. This study aims to identify the utility of stacked ensemble algorithm in predicting mortality.

Methods: The study was a cohort of adults admitted to a medical unit of a tertiary care hospital with sepsis. The data were divided into a training data set (70%) and a test data set (30%). Boruta algorithm was used to identify important features. In the first phase of stacked ensemble model, weak learners such as random forest (RF), support vector machine (SVM), elastic net, and gradient boosting machine were trained. The SVM was used in phase 2 as meta learner to combine the results of all weak learners. All models were validated using test data.

Results: In our cohort of 1,453 patients, the mortality rate was 27% (95% confidence interval [CI]: 25, 29). The Boruta algorithm identified inotrope use and assisted ventilation as the most important variables, which could predict mortality. The random forest outperforms (area under the curve [AUC]: 97.91%) the other algorithms. The AUCs for the other models are SVM (95.21%), GBM (93.67%), and GLM net (91.42%). However, the stacking of all the above models had an AUC of 92.14%. In the test data set, the accuracy of all methods including the RF method accuracy decreased (92.6 to 85.5%).

Conclusions: The random forest showed high accuracy in train and moderate accuracy in the test data. We suggest more regional open-access intensive care databases that can aid making machine learning a bigger support for healthcare personnel.