Fig. 1

Top nine rules for the rule-based model. Notes These plots depict the rules in the final ensemble as a binary tree structure. We chose to present the nine most important rules in the fitted ensemble. This could be obtained using the inbuilt plot method in the PRE algorithm. (1) The risk factors involved in each rule and their cut-off values are automatically determined by the rule-based model. The involved variables and cut-point employed by each rule are highlighted in bolden black: these individuals endorsing these criteria will follow the path of the prediction rule while those individuals not meeting the criteria instead are assigned as zero in the square box. (2) For example, 1st important rule indicates that those satisfying (1) disposition location ≤ 6, (2) metastatic cancer = 0, (3) leukemia = 0, (4) solid tumor = 0, are less likely to have 30-day readmission outcome. Abbreviations and descriptions of data element name: (1) DISPUNIFORMrr: Disposition of patient; (2) CANCER_METS: Elixhauser comorbdity measure of metastatic cancer; (3) CANCER_LEUK: Elixhauser comorbdity measure of leukemia; (4) CANCER_SOLID: Elixhauser comorbdity measure of solid tumor without metastasis, malignant; (5) pay1r: Expected primary payer; (6) mortal_score: Elixhauser Comorbidity Index Score; (7) HTN_CX: Elixhauser comorbdity measure of complicated hypertension; (8) DRUG_ABUSE: Elixhauser comorbdity measure of drug abuse; (9) H_Control: ownership/control of hospital; (10) LOS: length of stay; (11) I10_NPR: number of ICD-10-PCS procedures on this discharge; (12) Age: age at admission; (13) I10_NDX: number of ICD-10-CM diagnoses; (14) DIAB_CX: diabetes, complicated; (15) RENLFL_SEV: Elixhauser comorbdity measure of renal failure, severe; (16) APRDRG_Severity: severity of illness subclass by APR_DRG; (17) I10_NECAUSE: Number of ICD-10-CM External Cause of Morbidity codes on this record