Research on Time Window Prediction and Scoring Model for Trauma-Related Sepsis

Based on the MIMIC-III database of the Massachusetts Institute of Technology, this paper studies and analyzes the symptoms of trauma-related sepsis. Use SOFA score as the Inclusion and Exclusion Criteria, extract the relevant patient medical index data with the guidance of a professional clinician. Sequential forward search is applied to search the optimal index combination based on the eXtreme Gradient Boosting (XGBoost) algorithm. Twenty independent replicates perform to obtain 7 key risk indicators (Urea Nitrogen, Prothrombin Time, PO2, Sodium, Red Blood Cells, Carbon Dioxide, International Normalized Ratio). The time window prediction model builds by four machine learning algorithms (decision tree, random forest, decision tree-based adaptive reinforcement (Adaboost) algorithm, XGBoost). The results show that the time window prediction model of trauma-related sepsis has good generalization ability. The prediction effect of the random forest and XGBoost algorithm is better than the other two. Finally, using the multi-factor Logistic regression method build the risk scoring tool for sepsis-induced by trauma-related infection base on the key risk indicators and the opinions of professional clinicians. The results show that the data-driven risk scoring tool can effectively predict the outcome of patients with trauma-related sepsis, which has high clinical significance.