An integrated machine learning framework for hospital readmission prediction

Unplanned readmission (re-hospitalization) is the main source of cost for healthcare systems and is normally considered as an indicator of healthcare quality and hospital performance. Poor understanding of the relative importance of predictors and limited capacity of traditional statistical models challenge the development of accurate predictive models for readmission. This study aims to develop a robust and accurate risk prediction framework for hospital readmission, by combining feature selection algorithms and machine learning models. With regard to feature selection, an enhanced version of multi-objective bare-bones particle swarm optimization (EMOBPSO) is developed as the principal search strategy, and a new mutual information-based criterion is proposed to efficiently estimate feature relevancy and redundancy. A greedy local search strategy (GLS) is developed and merged into EMOBPSO to control the final feature subset size as desired. For the modeling process, manifold machine learning models, such as support vector machine, random forest, and deep neural network, are trained with preprocessed datasets and corresponding feature subsets. In the case study, the proposed methodology is applied to an actual hospital located in Northeast China, with various levels of data collected from the hospital information system. Results obtained from comparative experiments demonstrate the effectiveness of EMOBPSO and EMOBPSO-GLS feature selection algorithms. The combination of EMOBPSO (EMOBPSO-GLS) and deep neural network possesses robust predictive power among different datasets. Furthermore, insightful implications are abstracted from the obtained elite features and can be used by practitioners to determine the vulnerable patients for readmission and target the delivery of early resource-intensive interventions.