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.
random forest electronic health records early readmission penalized methods deep learning predictive models