Predicting hospital readmission for lupus patients: An RNN-LSTM-based deep-learning methodology

Hospital readmission is one of the critical metrics used for measuring the performance of hospitals. The HITECH Act imposes penalties when patients are readmitted to hospitals if they are diagnosed with one of the six conditions mentioned in the Act. However, patients diagnosed with lupus are the sixth highest in terms of rehospitalization. The heterogeneity in the disease and patient characteristics makes it very hard to predict rehospitalization. This research utilizes deep learning methods to predict rehospitalization within 30 days by extracting the temporal relationships in the longitudinal EHR clinical data. Prediction results from deep learning methods such as LSTM are evaluated and compared with traditional classification methods such as penalized logistic regression and artificial neural networks. The simple recurrent neural network method and its variant, gated recurrent unit network, are also developed and validated to compare their performance against the proposed LSTM model. The results indicated that the deep learning method RNN-LSTM has a significantly better performance (with an AUC of .70) compared to traditional classification methods such as ANN (with an AUC of 0.66) and penalized logistic regression (with an AUC of 0.63). The rationale for the better performance of the deep learning method may be due to its ability to leverage the temporal relationships of the disease state in patients over time and to capture the progression of the disease—relevant clinical information from patients' prior visits is carried forward in the memory, which may have enabled the higher predictability for the deep learning methods. •Lupus is a chronic autoimmune disease that impacts several system organ classes.•Predicting hospital readmissions for Lupus patients is a challenging task.•Longitudinal EHR and Deep Learning models are used to predict readmission of Lupus patients.•Sequential deep learning methods help capture the temporal relationships in the data.•Deep learning method of Recurrent Neural Networks – Long Short Term Memory produces the best prediction results with an AUC of 0.70 on holdout sample.