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.
Highlights
Electronic health record (EHR) data are accumulative, routinely collected patient observations from hospitals or clinical institutes
This criterion was set based on two considerations: (1) patients, who were discharged or died within the first 12 hours of intensive care unit (ICU) stay, were very unique patients that do not fit our clinical application and (2) our proposed self-correcting mechanism only starts from t = 7 onward, and we will need a number of time steps for the model to be stabilized
For the eICU dataset, our self-correcting recurrent neural networks (RNNs) with regularization model achieved 0.871 as area under ROC curve (AUC) and 0.808 as F1 score, corresponding to close to 10% improvement
Summary
Electronic health record (EHR) data are accumulative, routinely collected patient observations from hospitals or clinical institutes. We propose a self-correcting deep learning framework with accumulative ICU data to predict these acute conditions. 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. 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. The proposed self-correcting RNNs demonstrated effectiveness in AKI prediction and have the potential to be applied to clinical applications
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
