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@article{KOK2020103957,
title = {Automated prediction of sepsis using temporal convolutional network},
journal = {Computers in Biology and Medicine},
volume = {127},
pages = {103957},
year = {2020},
issn = {0010-4825},
doi = {https://doi.org/10.1016/j.compbiomed.2020.103957},
url = {https://www.sciencedirect.com/science/article/pii/S0010482520302912},
author = {Christopher Kok and V. Jahmunah and Shu Lih Oh and Xujuan Zhou and Raj Gururajan and Xiaohui Tao and Kang Hao Cheong and Rashmi Gururajan and Filippo Molinari and U.Rajendra Acharya},
keywords = {Sepsis, Machine learning, Deep learning, Prediction, per-patient metrics, per time-step metrics, 10-Fold validation, Temporal convolution network},
abstract = {Multiple organ failure is the trademark of sepsis. Sepsis occurs when the body's reaction to infection causes injury to its tissues and organs. As a consequence, fluid builds up in the tissues causing organ failure and leading to septic shock eventually. Some symptoms of sepsis include fever, arrhythmias, blood vessel leaks, impaired clotting, and generalised inflammation. In order to address the limitations in current diagnosis, we have proposed a cost-effective automated diagnostic tool in this study. A deep temporal convolution network has been developed for the prediction of sepsis. Septic data was fed to the model and a high accuracy and area under ROC curve (AUROC) of 98.8% and 98.0% were achieved respectively, for per time-step metrics. A relatively high accuracy and AUROC of 95.5% and 91.0% were also achieved respectively, for per-patient metrics. This is a novel study in that it has investigated per time-step metrics, compared to other studies which investigated per-patient metrics. Our model has also been evaluated by three validation methods. Thus, the recommended model is robust with high accuracy and precision and has the potential to be used as a tool for the prediction of sepsis in hospitals.}
}