构建新型冠状病毒感染患者肺部高分辨率CT (HRCT)多层次模型

DIidar Rashid, M. Faqe
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引用次数: 0

摘要

冠状病毒病,也被称为COVID-19,是由SARS-CoV-2病毒引起的。大多数感染病毒的人会出现轻至中度呼吸道疾病症状。本文的目的是通过多层次建模来构建这些冠状病毒患者的模型,我们从2019年9月1日至2022年2月1日期间(2019年9月1日至2022年2月1日)获得了7家医院,总计(636)名私立和公立医院患者,其中27%来自埃尔比勒,26%来自苏莱曼尼,23%来自杜霍克,24%来自哈拉布贾。在这些模型中,有限制的最大似然估计(RMLE)和完全最大似然估计(FML)对多层模型(固定和随机)的参数进行了适应估计。该应用程序是对患者的HRCT肺部,在伊拉克库尔德斯坦地区的县中随机选择了7家医院。结果表明,这三个变量在医院水平上都是显著的,但在两个最终模型中添加了与一级预测因子(吸烟者)相互作用的二级预测因子(医生经验),这远非显著。然而,糖尿病患者和做CT扫描之间有显著的关系,但吸烟和做CT扫描之间的关系并不显著。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Constructing a Multilevel Modeling to High-Resolution CT (HRCT) Lung in Patients with COVID-19 Infection
: The coronavirus disease, also called COVID-19 is caused by the SARS-CoV-2 virus. Most the people contaminated with the virus will experience mild to moderate symptoms of respiratory diseases. The aim of this paper is constructing a model by multilevel modeling for these patients who sufferers by coronaviruses, we got seven hospitals which totals (636) patients in private and public that 27% from Erbil, 26% from Sulaimani, 23% from Duhok and 24% from Halabja from the period (September 1th, 2019 to February 1th, 2022). In these modelling of multilevel restricted maximum likelihood estimation (RMLE) and full maximum likelihood (FML) acclimate estimate the parameters of multilevel models (fixed and random). The application was on the HRCT lungs of patients, seven hospitals were selected randomly among the county in Kurdistan region of Iraq. The result shows that all three variables are significant at the hospital level, but in the two final models add level-2 predictor (Doctor Experience) that interaction with level-1 predictor (smoker), which is far from significant. However, there is a significant relationship between being a diabetic and having a CT scan, but the relationship between smoking and having a CT scan is not significant.
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