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Open Access

Use of Deep Learning for Continuous Prediction of Mortality for All Admissions in Intensive Care Units

School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, China
Shenzhen Hospital, Southern Medical University, Shenzhen 518060, China
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Abstract

The mortality rate in the intensive care unit (ICU) is a key metric of hospital clinical quality. To enhance hospital performance, many methods have been proposed for the stratification of patients’ different risk categories, such as severity scoring systems and machine learning models. However, these methods make capturing time sequence information difficult, posing challenges to the continuous assessment of a patient’s severity during their hospital stay. Therefore, we built a predictive model that can make predictions throughout the patient’s stay and obtain the patient’s risk of death in real time. Our proposed model performed much better than other machine learning methods, including logistic regression, random forest, and XGBoost, in a full set of performance evaluation processes. Thus, the proposed model can support physicians’ decisions by allowing them to pay more attention to high-risk patients and anticipate potential complications to reduce ICU mortality.

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Tsinghua Science and Technology
Pages 639-648
Cite this article:
Zeng G, Zhuang J, Huang H, et al. Use of Deep Learning for Continuous Prediction of Mortality for All Admissions in Intensive Care Units. Tsinghua Science and Technology, 2023, 28(4): 639-648. https://doi.org/10.26599/TST.2022.9010027

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Received: 09 January 2022
Revised: 12 May 2022
Accepted: 05 July 2022
Published: 06 January 2023
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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