Model Interpretation
LTVA
To interpret the ML-based models, the SHAP method was used in this study. Specifically, we calculated the absolute mean SHAP values for each variable based on the CatBoost model applied to the internal validation set. For instance, with decreasing GCS scores and RBC_max values, the risk of in-hospital mortality of patients with LTVA increased. Moreover, the probability of in-hospital mortality increased with age and maximum central venous pressure values (Model interpretation based on the SHAP value. (A) SHAP summary plot. A dot is created for each feature attribution value for the model of each patient, and thus one patient is allocated one dot on the line for each feature. Dots are colored according to the values of features for the respective patient and accumulate vertically to depict density. The blue to red color of the y-axis represents the feature value (red: high; blue: low). The x-axis measures the impacts on the model output (right: positive; left: negative); the higher the SHAP value of a feature, the higher the probability of in-hospital mortality development. (B, C) SHAP dependency plots. SHAP values for specific features exceed zero, representing an increased risk of in-hospital mortality. RBC_max: the maximum red blood cell count; SHAP: Shapley additive explanation.