Fig. 4

The most important predictor variables in the XGBoost model¹. Notes ¹The bar graph represents the 10 most important predictor variables in the gradient boosting model sorted by importance. Features are shown ranked in decreasing importance order. The numbers represent the relative importance of each variable. The feature importance is calculated by the feature’s importance contribution relative to the most important feature. The variable importance measure uses the mean decrease in the Gini index to determine the contribution of each predictor variable. Abbreviations and descriptions of data element name: (1) APRDRG_Severity: Severity of Illness Subclass based on 3M All Patient Refined DRG; (2) I10-NDX: Number of ICD-10-CM diagnoses coded on the record; (3) mortal_score: Elixhauser Comorbidity Index Score; (4) AGE: the age at admission; (5) DISPUNIFORMrr: Disposition of patient; (6) I10_NPR: Number of procedures coded; (7) APRDRG_Risk_Mortality: Risk of Mortality Subclass based on 3M All Patient Refined DRG; (8) LOS: Length of Stay; (9) HTN_CX: Elixhauser comorbidity measure of complicated hypertension; (10) pay1r: Expected primary payer