Abstract: Objective To predict sepsis mortality in Intensive Care Unit(ICU)using a self-attention mechanism model. Methods Sepsis patients who meet the Sepsis-3 criteria were selected from the MIMIC-IV database. Multiple logistic regression analysis was performed to assess the impact of ethnicity on sepsis mortality. To further validate this relationship, parallel predictive models(with and without ethnicity)were constructed and their performance metrics were compared. The dataset was split into training and validation sets at a 1∶1 ratio. A binary cross-entropy loss function and the Adam optimizer were used to train the model for 1,000 iterations on the training set, and the models performance was evaluated on the validation set. Performance metrics included the area under the curve(AUC)of receiver operating characteristic(ROC)and accuracy. Results A total of 16,521 sepsis patients were included. The multiple logistic regression analysis showed no significant relationship between ethnicity and mortality, so ethnicity was not included in the subsequent model. Both models(with/without ethnicity)achieved identical AUC(0.82)and accuracy(0.88)on the validation set, outperforming traditional scoring systems(e.g., OASIS AUC: 0.70; LODS AUC: 0.74; SAPSII AUC: 0.75). Conclusion Ethnicity has no significant effect on the prediction of mortality in patients with sepsis. The prediction model built on the self-attention mechanism significantly improves the prediction performance of ICU sepsis mortality, outperforming traditional scoring systems.

Key words: Intensive Care Unit, Sepsis, Prediction, Mortality, Self-attention mechanism