A comparison of models for predicting early hospital readmissions

We compare a variety of models for predicting early hospital readmissions. Performance of existing models is insufficient for practical applications. Random forests and deep neural networks perform best in terms of AUC. Models fit to homogeneous patient subgroups typically outperform global models. Risk sharing arrangements between hospitals and payers together with penalties imposed by the Centers for Medicare and Medicaid (CMS) are driving an interest in decreasing early readmissions.