Explanation of COVID-۱۹ Mortality Using Artificial Neural Network Based on Underlying and Laboratory Risk Factors in Ilam, Iran

سال انتشار: 1401
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 45

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شناسه ملی سند علمی:

JR_ARCHRAZI-77-3_050

تاریخ نمایه سازی: 6 دی 1402

چکیده مقاله:

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 ۲۰۱۹ (COVID-۱۹). Data of ۲,۲۰۶ patients were extracted from the registry program of Shahid Mostafa Khomeini Hospital in Ilam, Iran, and were randomly analyzed in two training (۱,۵۴۴) and testing (۶۶۲) groups. By fitting different models of a three-layer neural network, ۱۲ variables could explain more than ۷۷% of the mortality variance in COVID-۱۹ 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-۱۹ mortality. The results also confirmed the power of a better explanation of ANN models to predict the mortality of patients.

نویسندگان

F Taghinezhad

Clinical Research Development Unit, Mostafa Khomeini Hospital, Ilam University of Medical Sciences, Ilam, Iran

M Kaffashian

Department of Physiology, School of Medicine, Ilam University of Medical Sciences, Ilam, Iran

Gh Kalvandi

Department of Pediatrics, School of Medicine, Besat Hospital, Hamadan University of Medical Sciences, Hamadan, Iran

E Shafiei

Non-Communicable Diseases Research Center, Ilam University of Medical Sciences, Ilam, Iran

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