Improving Sepsis Prediction Performance Using Conditional Recurrent Adversarial Networks

In this paper, we devise a novel method involving deep neural networks (DNNs) that improves the early prediction of sepsis for patients admitted to the intensive care units (ICUs). It is assumed that the patient data sets are dramatically corrupted by missing information, which negatively impacts the detection of the onset of sepsis. We propose a generative learning framework to estimate the missing information in data. Our model involves Conditional Generative Adversarial Networks (GANs) utilizing Long Short-Term Memory (LSTM) networks as the generator and discriminator when conditioned on class labels. A deep LSTM network is also employed for prediction purposes. The prediction network is trained with an output of the conditional GAN and evaluated on an unseen test set to investigate the performance of the proposed model. Here, we show that the proposed framework not only identifies long-term temporal dependencies but also exploits the missing patterns. We present the performance results and compare them to other well-known techniques. For the 4-hour, 8-hour, and 12-hour prediction of sepsis, the proposed method attains area under the receiver operating characteristic (AUROC) of 94.49%, 93.74%, and 94.01%, respectively. It is shown here that the improvement in imputation and prediction promises a highly effective method that can offer early detection of sepsis in high-risk patients