Big data-based grey forecast mathematical model to evaluate the effect of Escherichia coli infection on patients with lupus nephritis

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dc.contributor.authorFan, Maoxiao
dc.contributor.authorGu, Shuaishuai
dc.contributor.authorJin, Yansheng
dc.contributor.authorDing, Lan
dc.contributor.authorGhonaem, Eman
dc.contributor.authorArbab, Ahmed Mohamed Hamad
dc.date.accessioned2021-06-16T12:40:16Z
dc.date.available2021-06-16T12:40:16Z
dc.date.copyright2021
dc.date.issued2021-07
dc.description.abstractThe grey predictive mathematical model based on big data was used for analysis on the effect of Escherichia coli infection on patients with lupus nephritis (LN) in this study. Then, 156 patients diagnosed with LN infections by Wuzhong People's Hospital's information system (HIS) from October 30, 2017 to October 30, 2019 were selected as the experimental group, and 89 patients without LN infections were selected as the control group. Besides, the grey theory mathematical model was applied to process the integrated data, and feature analysis was employed to screen out disease-related bio-markers for the diagnosis of LN. The two groups were compared for affected organs, treatment, laboratory indicators, pathogenic bacteria, and recovery status. Multivariate logistic regression was used to analyze the related factors of patients with infections. The results showed that the specificity, sensitivity, and accuracy of the big data diagnosis based on the grey theory mathematical model were 78.9%, 87.6%, and 92.1, respectively; hormones, c-reactive protein, procalcitonin, and the daily antibiotic dose were positively correlated with concurrent infections (P < 0.05); 38 cases of Gram-negative bacteria were screened out, accounting for the largest proportion (37.18%); the effective rate of the experimental group was obviously lower than that of the control group (P < 0.05), suggesting that C-reactive protein (CRP), procalcitonin (PCT), antibiotics, daily dose of hormones, and serum albumin were independent risk factors for LN infection. In conclusion, the grey predictive mathematical model based on big data had high specificity, sensitivity, and accuracy in diagnosing the occurrence of infection in patients with LN; LN infection was mainly respiratory infection, and gram-negative bacteria were the main pathogen. Patients with LN infections showed higher serum creatinine, 24-hour urine protein quantification, CRP, and PCT, and lower serum albumin and recovery effect versus those without LN infections. © 2021 The Author(s)en_US
dc.identifier.citationFan, M., Gu, S., Jin, Y., Ding, L., Ghonaem, E., & Arbab, A. M. H. (2021). Big data-based Grey Forecast Mathematical Model to Evaluate the Effect of Escherichia Coli Infection on Patients with Lupus Nephritis. Results in Physics, 26, 104339. https://doi.org/10.1016/j.rinp.2021.104339en_US
dc.identifier.issn22113797
dc.identifier.urihttps://doi.org/10.1016/j.rinp.2021.104339
dc.identifier.urihttp://hdl.handle.net/20.500.12519/388
dc.language.isoenen_US
dc.publisherElsevier B.V.en_US
dc.relationAuthors Affiliations : Fan, M., Department of Nephrology, Wuzhong People's Hospital, 61 Dongwu North Road, Suzhou City 215128, Jiangsu Province, China; Gu, S., Department of Nephrology, Wuzhong People's Hospital, 61 Dongwu North Road, Suzhou City 215128, Jiangsu Province, China; Jin, Y., Department of Nephrology, Wuzhong People's Hospital, 61 Dongwu North Road, Suzhou City 215128, Jiangsu Province, China; Ding, L., Department of Nephrology, Wuzhong People's Hospital, 61 Dongwu North Road, Suzhou City 215128, Jiangsu Province, China; Ghonaem, E., Department of Social Sciences/Clinical Psychology, Faculty of Communication, Arts and Sciences, Canadian University Dubai, Dubai, United Arab Emirates; Arbab, A.M.H., Applied Science University, Al Eker, Bahrain
dc.relation.ispartofseriesResults in Physics;Volume 26
dc.rightsCreative Commons CC-BY-NC-ND License
dc.rights.holderCopyright : © 2021 The Author(s)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectBio-markeren_US
dc.subjectEscherichia colien_US
dc.subjectGram-negative bacteriaen_US
dc.subjectGrey theory predictionen_US
dc.subjectLupus nephritisen_US
dc.titleBig data-based grey forecast mathematical model to evaluate the effect of Escherichia coli infection on patients with lupus nephritisen_US
dc.typeArticleen_US

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