Self-Correcting Recurrent Neural Network for Acute Kidney Injury Prediction in Critical Care

Hao Du; Ziyuan Pan; Kee Yuan Ngiam; Fei Wang; Ping Shum; Mengling Feng

doi:10.34133/2021/9808426

Self-Correcting Recurrent Neural Network for Acute Kidney Injury Prediction in Critical Care

Health Data Sci. 2021 Dec 23:2021:9808426. doi: 10.34133/2021/9808426. eCollection 2021.

Authors

Hao Du¹, Ziyuan Pan², Kee Yuan Ngiam³, Fei Wang⁴, Ping Shum², Mengling Feng¹

Affiliations

¹ Saw Swee Hock School of Public Health, National University of Singapore, Singapore.
² School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore.
³ National University Health System, Singapore.
⁴ Population Health Sciences, Weill Cornell Medical College, Cornell University, New York, USA.

Abstract

Background. In critical care, intensivists are required to continuously monitor high-dimensional vital signs and lab measurements to detect and diagnose acute patient conditions, which has always been a challenging task. Recently, deep learning models such as recurrent neural networks (RNNs) have demonstrated their strong potential on predicting such events. However, in real deployment, the patient data are continuously coming and there is no effective adaptation mechanism for RNN to incorporate those new data and become more accurate.Methods. In this study, we propose a novel self-correcting mechanism for RNN to fill in this gap. Our mechanism feeds prediction errors from the predictions of previous timestamps into the prediction of the current timestamp, so that the model can "learn" from previous predictions. We also proposed a regularization method that takes into account not only the model's prediction errors on the labels but also its estimation errors on the input data.Results. We compared the performance of our proposed method with the conventional deep learning models on two real-world clinical datasets for the task of acute kidney injury (AKI) prediction and demonstrated that the proposed model achieved an area under ROC curve at 0.893 on the MIMIC-III dataset and 0.871 on the Philips eICU dataset.Conclusions. The proposed self-correcting RNNs demonstrated effectiveness in AKI prediction and have the potential to be applied to clinical applications.