ارسل ملاحظاتك

ارسل ملاحظاتك لنا







Constructing a Multilevel Modeling to High-Resolution CT (HRCT) Lung in Patients with COVID-19 Infection

المصدر: المجلة العراقية للعلوم الإحصائية
الناشر: جامعة الموصل - كلية علوم الحاسوب والرياضيات
المؤلف الرئيسي: Aziz, Didar Abdal Wafa Rashid (Author)
مؤلفين آخرين: Faqe, Mohammad Mahmood (Co-Author)
المجلد/العدد: ع36
محكمة: نعم
الدولة: العراق
التاريخ الميلادي: 2022
الصفحات: 117 - 137
ISSN: 1680-855X
رقم MD: 1339288
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: EcoLink
مواضيع:
كلمات المؤلف المفتاحية:
Multilevel Modeling | Fixed Effect | Random Effect | Interclass Correlation
رابط المحتوى:
صورة الغلاف QR قانون
حفظ في:
المستخلص: 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.

ISSN: 1680-855X