Continuous and automatic mortality risk prediction using vital signs in the intensive care unit: a hybrid neural network approach

Baker, Stephanie, Xiang, Wei, and Atkinson, Ian (2020) Continuous and automatic mortality risk prediction using vital signs in the intensive care unit: a hybrid neural network approach. Scientific Reports, 10. 21282.

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Abstract

Mortality risk prediction can greatly improve the utilization of resources in intensive care units (ICUs). Existing schemes in ICUs today require laborious manual input of many complex parameters. In this work, we present a scheme that uses variations in vital signs over a 24-h period to make mortality risk assessments for 3-day, 7-day, and 14-day windows. We develop a hybrid neural network model that combines convolutional (CNN) layers with bidirectional long short-term memory (BiLSTM) to predict mortality from statistics describing the variation of heart rate, blood pressure, respiratory rate, blood oxygen levels, and temperature. Our scheme performs strongly compared to state-of-the-art schemes in the literature for mortality prediction, with our highest-performing model achieving an area under the receiver-operator curve of 0.884. We conclude that the use of a hybrid CNN-BiLSTM network is highly effective in determining mortality risk for the 3, 7, and 14 day windows from vital signs. As vital signs are routinely recorded, in many cases automatically, our scheme could be implemented such that highly accurate mortality risk could be predicted continuously and automatically, reducing the burden on healthcare providers and improving patient outcomes.

Item ID: 65938
Item Type: Article (Research - C1)
ISSN: 2045-2322
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Copyright Information: © The Author(s) 2020. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/.
Date Deposited: 03 Feb 2021 03:00
FoR Codes: 40 ENGINEERING > 4003 Biomedical engineering > 400309 Neural engineering @ 100%
SEO Codes: 92 HEALTH > 9202 Health and Support Services > 920204 Evaluation of Health Outcomes @ 80%
92 HEALTH > 9204 Public Health (excl. Specific Population Health) > 920408 Health Status (e.g. Indicators of Well-Being) @ 20%
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