Display Omitted 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. There are a number of published risk models predicting 30day readmissions for particular patient populations, however they often exhibit poor predictive performance and would be unsuitable for use in a clinical setting. In this work we describe and compare several predictive models, some of which have never been applied to this task and which outperform the regression methods that are typically applied in the healthcare literature. In addition, we apply methods from deep learning to the five conditions CMS is using to penalize hospitals, and offer a simple framework for determining which conditions are most cost effective to target.
['Joseph Futoma', 'Jonathan Morris', 'Joseph E. Lucas']
A comparison of models for predicting early hospital readmissions