Automated prediction of sepsis using temporal convolutional network

Kok, Christopher and Jahmunah, V. and Oh, Shu Lih and Zhou, Xujuan and Gururajan, Raj and Tao, Xiaohui ORCID: https://orcid.org/0000-0002-0020-077X and Cheong, Kang Hao and Gururajan, Rashmi and Molinari, Filippo and Acharya, U. Rajendra (2020) Automated prediction of sepsis using temporal convolutional network. Computers in Biology and Medicine:103957. pp. 1-23. ISSN 0010-4825


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.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current - Faculty of Business, Education, Law and Arts - School of Management and Enterprise (1 July 2013 -)
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sept 2019 -)
Date Deposited: 08 Sep 2020 04:44
Last Modified: 14 Sep 2020 01:23
Uncontrolled Keywords: sepsis; machine learning; deep learning; prediction; per-patient metrics; per time-step metrics; 10-fold validation; temporal convolution network
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.1016/j.compbiomed.2020.103957
URI: http://eprints.usq.edu.au/id/eprint/39295

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