A study of Generalizability of Recurrent Neural Network-Based Predictive Models for Heart Failure Onset Risk using a Large and Heterogeneous EHR Data set

In this study, we evaluated an RNN model, RETAIN, over Cerner Health Facts® EMR data, for heart failure onset risk prediction. Our data set included over 150,000 heart failure patients and over 1,000,000 controls from nearly 400 hospitals. Convincingly, RETAIN achieved an AUC of 82% in comparison to an AUC of 79% for logistic regression, demonstrating the power of more expressive deep learning models for EHR predictive modeling. The prediction performance fluctuated across different patient groups and varied from hospital to hospital. Also, we trained RETAIN models on individual hospitals and found that the model can be applied to other hospitals with only about 3.6% of reduction of AUC. Our results demonstrated the capability of RNN for predictive modeling with large and heterogeneous EHR data, and pave the road for future improvements. Graphical abstract
Source: Journal of Biomedical Informatics - Category: Information Technology Source Type: research