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Application of machine learning risk prediction mathematical model in the diagnosis of Escherichia coli infection in patients with septic shock by cardiovascular color doppler ultrasound

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dc.contributor.author Shen, Hualiang
dc.contributor.author Hu, Yinfeng
dc.contributor.author Liu, Xiatian
dc.contributor.author Jiang, Zhenzhen
dc.contributor.author Ye, Hongwei
dc.contributor.author Takshe, Aseel A.
dc.contributor.author Al Dulaimi, Saeed Hameed Kurdi
dc.date.accessioned 2021-06-17T06:47:16Z
dc.date.available 2021-06-17T06:47:16Z
dc.date.copyright 2021
dc.date.issued 2021-07
dc.identifier.citation Shen, H., Hu, Y., Liu, X., Jiang, Z., Ye, H., Takshe, A., & Al Dulaimi, S. H. K. (2021). Application of Machine Learning Risk Prediction Mathematical Model in the Diagnosis of Escherichia Coli Infection in Patients with Septic Shock by Cardiovascular Color Doppler Ultrasound. Results in Physics, 26, 104368. https://doi.org/10.1016/j.rinp.2021.104368 en_US
dc.identifier.issn 22113797
dc.identifier.uri https://doi.org/10.1016/j.rinp.2021.104368
dc.identifier.uri http://hdl.handle.net/20.500.12519/393
dc.description This article is not available at CUD collection. The version of scholarly record of this article is published in Results in Physics (2021), available online at: https://doi.org/10.1016/j.rinp.2021.104368 en_US
dc.description.abstract this study was to explore the diagnosis of septic shock patients with Escherichia coli (E. coli) infection based on cardiovascular color Doppler ultrasound (CCDUS) images under the machine learning risk prediction mathematical model (risk prediction model). 120 septic shock patients with Escherichia coli (E. coli) infection, admitted to xxx hospital were selected as research subjects, and they were randomly divided into experimental group and control group, including 76 males and 44 females, with an average age of (45.47 ± 11.35) years old. The prediction model, random forest mathematical model (RF model), and feature combination were trained and applied in the CCDUS. The error rate, F1-score, and area under the curve (AUC) were compared. It was found that the prediction effect of the risk prediction model was better (P < 0.05). The receiver operating characteristic curve (ROC) was drawn based on the risk prediction model, and it was found that the AUC was 0.924, and the best cutoff value was 0.247. The consistency test between the predicted death result and the actual result showed that Kappa = 0.824, which was higher than 0.75. The pathogenic microorganisms of the patients were mainly Gram-positive bacteria (GPB) in 32 cases (53.33%). There were 19 cases whose pathogenic bacteria was E. coli, and 11 cases (57.9%) of which were acquired in the intensive care unit (ICU). The patient mortality rate was 41.67%. Finally, the acute physiology and chronic health II (APACH II) score and D-dimer of the patients were substituted into the Logistic regression model. The effect of the risk prediction model was better than the RF model and feature combination; the measurement results based on the risk prediction model had good consistency; the D-dimer and APACH II score were independent factors for death of the septic shock. © 2021 The Author(s) en_US
dc.language.iso en en_US
dc.publisher Elsevier B.V. en_US
dc.relation Authors Affiliations : Shen, H., Department of Ultrasound, Shaoxing People's Hospital (Shaoxing Hospital, Zhejiang University School of Medicine), Shaoxing City 312000, Zhejiang Province, China; Hu, Y., Department of Ultrasound, The Ningbo First Hospital, Ningbo City 315010, Zhejiang Province, China; Liu, X., Department of Ultrasound, Shaoxing People's Hospital (Shaoxing Hospital, Zhejiang University School of Medicine), Shaoxing City 312000, Zhejiang Province, China; Jiang, Z., Department of Ultrasound, Shaoxing People's Hospital (Shaoxing Hospital, Zhejiang University School of Medicine), Shaoxing City 312000, Zhejiang Province, China; Ye, H., Department of Critical Care Medicine, Changshu Hospital Affiliated to Soochow University, Changshu City 215500, Jiangsu Province, China; Takshe, A., Department of Environmental Health Sciences, Faculty of Communication, Arts and Sciences, Canadian University DubaiDubai, United Arab Emirates; Al Dulaimi, S.H.K., Applied Science University, Al Eker, Bahrain
dc.relation.ispartofseries Results in Physics;Volume 26
dc.rights Creative Commons CC-BY-NC-ND License
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Cardiovascular color Doppler ultrasound en_US
dc.subject E. coli en_US
dc.subject Machine learning en_US
dc.subject Mathematical model en_US
dc.subject Rick prediction en_US
dc.subject Septic shock en_US
dc.title Application of machine learning risk prediction mathematical model in the diagnosis of Escherichia coli infection in patients with septic shock by cardiovascular color doppler ultrasound en_US
dc.type Article en_US
dc.rights.holder Copyright : © 2021 The Author(s)


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