A mixed-ensemble model for hospital readmission.

Objective: A hospital readmission is defined as an admission to a hospital within a certain time frame, typically thirty days, following a previous discharge, either to the same or to a different hospital. Because most patients are not readmitted, the readmission classification problem is highly imbalanced.

Materials And Methods: We developed a hospital readmission predictive model, which enables controlling the tradeoff between reasoning transparency and predictive accuracy, by taking into account the unique characteristics of the learned database. A boosted C5.0 tree, as the base classifier, was ensembled with a support vector machine (SVM), as a secondary classifier. The models were induced and validated using anonymized administrative records of 20,321 inpatient admissions, of 4840 Congestive Heart Failure (CHF) patients, at the Veterans Health Administration (VHA) hospitals in Pittsburgh, from fiscal years (FY) 2006 through 2014.

Results: The SVM predictions are characterized by greater sensitivity values (true positive rates) than are the C5.0 predictions, for a wider range of cut off values of the ROC curve, depending on a predefined confidence threshold for the base C5.0 classifier. The total accuracy for the ensemble ranges from 81% to 85%. Different predictors, including comorbidities, lab values, and vitals, play different roles in the two models.

Conclusions: The mixed-ensemble model enables easy and fast exploratory knowledge discovery of the database, and a control of the classification error for positive readmission instances. Implementation of this ensembling method for predicting all-cause hospital readmissions of CHF patients allows overcoming some of the limitations of the classifiers considered individually, and of other traditional ensembling methods. It also increases the classification accuracy for positive readmission instances, particularly when strong predictors are not available.