AUTHOR=Su Longxiang , Xu Zheng , Chang Fengxiang , Ma Yingying , Liu Shengjun , Jiang Huizhen , Wang Hao , Li Dongkai , Chen Huan , Zhou Xiang , Hong Na , Zhu Weiguo , Long Yun 

TITLE=Early Prediction of Mortality, Severity, and Length of Stay in the Intensive Care Unit of Sepsis Patients Based on Sepsis 3.0 by Machine Learning Models

JOURNAL=Frontiers in Medicine

VOLUME=Volume 8 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2021.664966

DOI=10.3389/fmed.2021.664966

ISSN=2296-858X

ABSTRACT=Background Early prediction of the clinical outcome of patients with sepsis is of great significance, which can reduce the mortality of patients. But it is clinically difficult for clinicians.
Methods A total of 2,224 patients with sepsis were involved over a 3-year period (2016– 2018) to the intensive care unit (ICU) of Peking Union Medical College Hospital. With all the key medical data from the first six-hour in ICU, three machine learning models including logistic regression, random forest and XGBoost were carried out to predict the mortality, severity (sepsis/septic shock), and length of ICU stay (LOS) (>6 days, ≤ 6days). Missing data imputation and oversampling were completed on the dataset before putting them into model. 
Results Compared to mortality and LOS prediction, the severity prediction achieves the best classification results (sensitivity = 0.65, specificity = 0.73, F1 score = 0.72, AUC = 0.79) with random forest classifier according to the area under operating receiver characteristics (AUC). Random forest model also showed the best performance (mortality prediction: sensitivity = 0.50, specificity = 0.84, F1 score = 0.66, AUC = 0.74; LOS prediction: sensitivity = 0.79, specificity = 0.66, F1 score = 0.69, AUC = 0.76) among three models. While the predictive ability of SOFA score is inferior to the above three models.
Conclusions Using the random forest classifier in the first 6 hours of ICU admission can provide comprehensive early warning of sepsis, which contribute to the formulation and management of clinical decisions and the allocation and management of resources.