A machine-learning approach for prediction of hospital mortality in cancer-related sepsis

To develop a machine learning model to predict hospital mortality and identify risk factors in cancer-related sepsis patients. We obtained data from the Medical Information Mart for Intensive Care (MIMIC)-IV critical care data set, which included patients who diagnosed with cancer and fulfilled the definition of sepsis between 2008 and 2019. The data set was randomly split into a training set and a validation set. The dataset was imputed using the K-Nearest Neighbor (KNN) imputation model. An advanced machine learning model called CatBoost was established and then assessed by SHAP value. A total of 5081 patients were included in the final analysis. The cancer-related sepsis patients had a lower hospital survival (13.8% vs. 25.3%, P