Fig. 3

The top 10 most important predictive factors for the random forest. Notes A higher "Mean Decrease in Gini" in the x-axis indicates a higher purity (less noise, less bias) contributed by the variable, and higher variable importance). ¹Variable importance is reported as two measures: it is based on the mean decrease of accuracy in prediction on the out of bag sampled when a given variable is excluded; it is also computed using the mean decrease in Gini index. The variable importance for each variable is expressed relative to the largest. Abbreviations and descriptions of data element name: (1) APRDRG_Severity: Severity of Illness Subclass based on 3M All Patient Refined DRG; (2) mortal_score: Elixhauser Comorbidity Index Score for in-hospital mortality; (3) I10-NDX: Number of ICD-10-CM diagnoses coded on the record; (4) APRDRG_Risk_Mortality: Risk of Mortality Subclass based on 3M All Patient Refined DRG; (5) DISPUNIFORMrr: Disposition of patient; (6) AGE: the age at admission; (7) I10_NPR: Number of procedures coded; (8) LOS: Length of Stay (9) CANCER_METS: Elixhauser comorbidity measure of metastatic cancer; (10) HTN_CX: hypertension, complicated; (11) PL_NCHS: Patient location based on National Center for Health Statistics (NCHS) urban–rural classification scheme for U.S. counties; (12) DQTR: The quarter of Discharge time