Development and validation of an interpretable machine learning model for predicting cognitive impairment in patients with sepsis

Using SHAP analysis, the RF model provided clear insights into the key factors contributing to the model's prediction of cognitive impairment after sepsis. The model not only achieved high predictive accuracy but also offered a transparent, data-driven tool to identify patients at elevated risk, potentially enabling timely interventions and tailored clinical management.