Predicting Hospital Readmission: A Joint Ensemble-Learning Model

Hospital readmission is among the most critical issues in the healthcare system due to its high prevalence and cost. The improvement effort necessitates reliable prediction models which can identify high-risk patients effectively and enable healthcare practitioners to take a strategic approach. Using predictive analytics based on electronic health record (EHR) for hospital readmission is faced with multiple challenges such as high dimensionality and event sparsity of medical codes and the class imbalance. To response to these challenges, an analytical framework is proposed by data-driven approaches using hospital inpatient administrative data from a nationwide healthcare dataset. A joint ensemble-learning model, which combines the modified weight boosting algorithm with stacking algorithm, is developed and validated. Our study first explores the effects of different feature engineering methods, which effectively handles the challenge of medical vector representation and medical vector sparsity. Secondly, ensemble learning with the proposed modified weight boosting algorithm is used to tackle the class imbalance problem and improve predictability. Finally, we provide various misclassification costs by setting different weights for each class during model training. Using the framework with the proposed modified weight boosting algorithm improves overall model performance by 22.7% and recall from 0.726 to the highest of 0.891 comparing to the benchmark models. Hospital practitioners can also utilize the prediction results of different cost weight to select the most suitable readmission intervention for patients according to the penalty policy of Centers for Medicare and Medicaid Services (CMS) and the cost trade-off of their hospitals.