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Taghinezhad, F, Kaffashian, M, Kalvandi, Gh, Shafiei, E. (1401). Explanation of COVID-19 Mortality Using Artificial Neural Network Based on Underlying and Laboratory Risk Factors in Ilam, Iran. سامانه مدیریت نشریات علمی, 77(3), 1327-1332. doi: 10.22092/ari.2021.355438.1684
F Taghinezhad; M Kaffashian; Gh Kalvandi; E Shafiei. "Explanation of COVID-19 Mortality Using Artificial Neural Network Based on Underlying and Laboratory Risk Factors in Ilam, Iran". سامانه مدیریت نشریات علمی, 77, 3, 1401, 1327-1332. doi: 10.22092/ari.2021.355438.1684
Taghinezhad, F, Kaffashian, M, Kalvandi, Gh, Shafiei, E. (1401). 'Explanation of COVID-19 Mortality Using Artificial Neural Network Based on Underlying and Laboratory Risk Factors in Ilam, Iran', سامانه مدیریت نشریات علمی, 77(3), pp. 1327-1332. doi: 10.22092/ari.2021.355438.1684
Taghinezhad, F, Kaffashian, M, Kalvandi, Gh, Shafiei, E. Explanation of COVID-19 Mortality Using Artificial Neural Network Based on Underlying and Laboratory Risk Factors in Ilam, Iran. سامانه مدیریت نشریات علمی, 1401; 77(3): 1327-1332. doi: 10.22092/ari.2021.355438.1684

Explanation of COVID-19 Mortality Using Artificial Neural Network Based on Underlying and Laboratory Risk Factors in Ilam, Iran

Archives of Razi Institute
مقاله 52، دوره 77، شماره 3، مرداد و شهریور 2022، صفحه 1327-1332    اصل مقاله (522.96 K)
نوع مقاله: Short Communication
شناسه دیجیتال (DOI): 10.22092/ari.2021.355438.1684
نویسندگان
F Taghinezhad1؛ M Kaffashian2؛ Gh Kalvandi3؛ E Shafiei* 4
1Clinical Research Development Unit, Mostafa Khomeini Hospital, Ilam University of Medical Sciences, Ilam, Iran
2Department of Physiology, School of Medicine, Ilam University of Medical Sciences, Ilam, Iran
3Department of Pediatrics, School of Medicine, Besat Hospital, Hamadan University of Medical Sciences, Hamadan, Iran
4Non-Communicable Diseases Research Center, Ilam University of Medical Sciences, Ilam, Iran
چکیده
The spread of new waves of coronavirus outbreaks, high mortality rates, and time-consuming and numerous challenges in achieving collective safety through vaccination and the need to prioritize the allocation of vaccines to the general population have led to the continued identification of risk factors associated with mortality in patients through innovative strategies and new statistical models. In this study, an artificial neural network (ANN) model was used to predict morbidity in patients with coronavirus disease 2019 (COVID-19). Data of 2,206 patients were extracted from the registry program of Shahid Mostafa Khomeini Hospital in Ilam, Iran, and were randomly analyzed in two training (1,544) and testing (662) groups. By fitting different models of a three-layer neural network, 12 variables could explain more than 77% of the mortality variance in COVID-19 patients. These findings could be used to better mortality management, vaccination prioritization, public education, and quarantine, and allocation of intensive care beds to reduce COVID-19 mortality. The results also confirmed the power of a better explanation of ANN models to predict the mortality of patients.
کلیدواژه‌ها
Artificial neural networks؛ COVID-19؛ Multilayer perceptron؛ Iran
عنوان مقاله [English]
تبین مرگ و میر ناشی از COVID-19 با استفاده از شبکه عصبی مصنوعی (ANN) بر اساس عوامل خطر زمینه ای و آزمایشگاهی در ایلام ، ایران
چکیده [English]
گسترش موج های جدید شیوع ویروس کرونا ، میزان مرگ و میر بالا ، و چالش های وقت گیر و متعدد برای دستیابی به ایمنی جمعی از طریق واکسیناسیون و نیاز به اولویت بندی تخصیص واکسن ها در جمعیت عمومی ، منجر به شناسایی عوامل خطر مرتبط با مرگ و میر در بیماران از طریق استراتژی های بدیع و مدل های آماری جدید شده است. در این مطالعه ، از مدل شبکه عصبی مصنوعی (ANN) برای پیش بینی عوارض در بیماران مبتلا به COVID-19 استفاده شد. داده های 2206 بیمار از برنامه ثبت ریجستری بیمارستان شهید مصطفی خمینی ایلام ، استخراج شد و به طور تصادفی در دو گروه آموزشی (1544) و آزمایش (662) مورد تجزیه و تحلیل قرار گرفت. با استفاده از مدل های مختلف یک شبکه عصبی سه لایه ، 12 متغیر می توانند بیش از 77 درصد از واریانس مرگ و میر در بیماران COVID-19 را توضیح دهند. این یافته ها می تواند برای مدیریت بهتر مرگ و میر ، اولویت بندی واکسیناسیون ، آموزش عمومی و قرنطینه و اختصاص تخت های مراقبت های ویژه برای کاهش مرگ و میر ناشی از COVID-19 مورد استفاده قرار گیرد. این یافته ها همچنین قدرت تبیین بهتر مدلهای ANN را برای پیش بینی مرگ و میر بیماران تأیید کرد
کلیدواژه‌ها [English]
شبکه های عصبی مصنوعی, COVID-19((کروناویروس), پرسپترون چند لایه, ایران
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