Early Prediction of COVID-19 Patient Survival by Blood Plasma Using Machine Learning

Yibo Zhu; Xiumin Shi; Yan Wang; Yixuan Zhu; Lu Wang

doi:10.1109/CCET59170.2023.10335125

Early Prediction of COVID-19 Patient Survival by Blood Plasma Using Machine Learning

Yibo Zhu, Xiumin Shi, Yan Wang, Yixuan Zhu, Lu Wang

School of Information and Electronics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

The Coronavirus Disease 2019 (COVID-19) pandemic has severely disrupted the global healthcare and medical system. Although COVID-19 is no longer considered a public health emergency of international concern, it can still cause many infections and even life-threatening conditions. This study aims to reveal novel potential biomarkers of mortality and identify associated mechanisms of death caused by COVID-19 using machine learning approaches to re-analyze metabolomics data. We found that the combination of NG,NG-Dimethyl-L-arginine, 4-Coumaryl alcohol, Pyridoxamine, N1,N12-Diacetylspermine, Coniferyl alcohol, 1-Phosphatidy1-1D-myo-inositol 3-phosphate and sn-Glycero-3-phosphocholine is an effective biomarker for survival prediction of severe COVID-19 patients. These metabolites suggest potential immunomodulatory therapeutic strategies for the treatment of COVID-19.

Original language	English
Title of host publication	2023 IEEE 6th International Conference on Computer and Communication Engineering Technology, CCET 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	11-15
Number of pages	5
ISBN (Electronic)	9798350340686
DOIs	https://doi.org/10.1109/CCET59170.2023.10335125
Publication status	Published - 2023
Event	6th IEEE International Conference on Computer and Communication Engineering Technology, CCET 2023 - Beijing, China Duration: 4 Aug 2023 → 6 Aug 2023

Publication series

Name	2023 IEEE 6th International Conference on Computer and Communication Engineering Technology, CCET 2023

Conference

Conference	6th IEEE International Conference on Computer and Communication Engineering Technology, CCET 2023
Country/Territory	China
City	Beijing
Period	4/08/23 → 6/08/23

Keywords

COVID-19
biomarkers
machine learning
metabolomics analysis
survival prediction

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/CCET59170.2023.10335125

Cite this

Zhu, Y., Shi, X., Wang, Y., Zhu, Y., & Wang, L. (2023). Early Prediction of COVID-19 Patient Survival by Blood Plasma Using Machine Learning. In 2023 IEEE 6th International Conference on Computer and Communication Engineering Technology, CCET 2023 (pp. 11-15). (2023 IEEE 6th International Conference on Computer and Communication Engineering Technology, CCET 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCET59170.2023.10335125

Zhu, Yibo ; Shi, Xiumin ; Wang, Yan et al. / Early Prediction of COVID-19 Patient Survival by Blood Plasma Using Machine Learning. 2023 IEEE 6th International Conference on Computer and Communication Engineering Technology, CCET 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 11-15 (2023 IEEE 6th International Conference on Computer and Communication Engineering Technology, CCET 2023).

@inproceedings{300237f2553e41828bfb03b16bd776b5,

title = "Early Prediction of COVID-19 Patient Survival by Blood Plasma Using Machine Learning",

abstract = "The Coronavirus Disease 2019 (COVID-19) pandemic has severely disrupted the global healthcare and medical system. Although COVID-19 is no longer considered a public health emergency of international concern, it can still cause many infections and even life-threatening conditions. This study aims to reveal novel potential biomarkers of mortality and identify associated mechanisms of death caused by COVID-19 using machine learning approaches to re-analyze metabolomics data. We found that the combination of NG,NG-Dimethyl-L-arginine, 4-Coumaryl alcohol, Pyridoxamine, N1,N12-Diacetylspermine, Coniferyl alcohol, 1-Phosphatidy1-1D-myo-inositol 3-phosphate and sn-Glycero-3-phosphocholine is an effective biomarker for survival prediction of severe COVID-19 patients. These metabolites suggest potential immunomodulatory therapeutic strategies for the treatment of COVID-19.",

keywords = "COVID-19, biomarkers, machine learning, metabolomics analysis, survival prediction",

author = "Yibo Zhu and Xiumin Shi and Yan Wang and Yixuan Zhu and Lu Wang",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 6th IEEE International Conference on Computer and Communication Engineering Technology, CCET 2023 ; Conference date: 04-08-2023 Through 06-08-2023",

year = "2023",

doi = "10.1109/CCET59170.2023.10335125",

language = "English",

series = "2023 IEEE 6th International Conference on Computer and Communication Engineering Technology, CCET 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "11--15",

booktitle = "2023 IEEE 6th International Conference on Computer and Communication Engineering Technology, CCET 2023",

address = "United States",

}

Zhu, Y, Shi, X, Wang, Y, Zhu, Y & Wang, L 2023, Early Prediction of COVID-19 Patient Survival by Blood Plasma Using Machine Learning. in 2023 IEEE 6th International Conference on Computer and Communication Engineering Technology, CCET 2023. 2023 IEEE 6th International Conference on Computer and Communication Engineering Technology, CCET 2023, Institute of Electrical and Electronics Engineers Inc., pp. 11-15, 6th IEEE International Conference on Computer and Communication Engineering Technology, CCET 2023, Beijing, China, 4/08/23. https://doi.org/10.1109/CCET59170.2023.10335125

Early Prediction of COVID-19 Patient Survival by Blood Plasma Using Machine Learning. / Zhu, Yibo; Shi, Xiumin; Wang, Yan et al.
2023 IEEE 6th International Conference on Computer and Communication Engineering Technology, CCET 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 11-15 (2023 IEEE 6th International Conference on Computer and Communication Engineering Technology, CCET 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Zhu, Yibo

AU - Shi, Xiumin

AU - Wang, Yan

AU - Zhu, Yixuan

AU - Wang, Lu

PY - 2023

Y1 - 2023

N2 - The Coronavirus Disease 2019 (COVID-19) pandemic has severely disrupted the global healthcare and medical system. Although COVID-19 is no longer considered a public health emergency of international concern, it can still cause many infections and even life-threatening conditions. This study aims to reveal novel potential biomarkers of mortality and identify associated mechanisms of death caused by COVID-19 using machine learning approaches to re-analyze metabolomics data. We found that the combination of NG,NG-Dimethyl-L-arginine, 4-Coumaryl alcohol, Pyridoxamine, N1,N12-Diacetylspermine, Coniferyl alcohol, 1-Phosphatidy1-1D-myo-inositol 3-phosphate and sn-Glycero-3-phosphocholine is an effective biomarker for survival prediction of severe COVID-19 patients. These metabolites suggest potential immunomodulatory therapeutic strategies for the treatment of COVID-19.

AB - The Coronavirus Disease 2019 (COVID-19) pandemic has severely disrupted the global healthcare and medical system. Although COVID-19 is no longer considered a public health emergency of international concern, it can still cause many infections and even life-threatening conditions. This study aims to reveal novel potential biomarkers of mortality and identify associated mechanisms of death caused by COVID-19 using machine learning approaches to re-analyze metabolomics data. We found that the combination of NG,NG-Dimethyl-L-arginine, 4-Coumaryl alcohol, Pyridoxamine, N1,N12-Diacetylspermine, Coniferyl alcohol, 1-Phosphatidy1-1D-myo-inositol 3-phosphate and sn-Glycero-3-phosphocholine is an effective biomarker for survival prediction of severe COVID-19 patients. These metabolites suggest potential immunomodulatory therapeutic strategies for the treatment of COVID-19.

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KW - biomarkers

KW - machine learning

KW - metabolomics analysis

KW - survival prediction

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DO - 10.1109/CCET59170.2023.10335125

M3 - Conference contribution

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T3 - 2023 IEEE 6th International Conference on Computer and Communication Engineering Technology, CCET 2023

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BT - 2023 IEEE 6th International Conference on Computer and Communication Engineering Technology, CCET 2023

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Y2 - 4 August 2023 through 6 August 2023

ER -

Zhu Y, Shi X, Wang Y, Zhu Y, Wang L. Early Prediction of COVID-19 Patient Survival by Blood Plasma Using Machine Learning. In 2023 IEEE 6th International Conference on Computer and Communication Engineering Technology, CCET 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 11-15. (2023 IEEE 6th International Conference on Computer and Communication Engineering Technology, CCET 2023). doi: 10.1109/CCET59170.2023.10335125

Early Prediction of COVID-19 Patient Survival by Blood Plasma Using Machine Learning

Abstract

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Keywords

UN SDGs

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