Medical Journal of The Islamic Republic of Iran (MJIRI)
Iran University of Medical Sciences
Thu, Nov 21, 2024
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Volume 36, Issue 1 (1-2022)
Med J Islam Repub Iran 2022 |
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Taheriyan M, Ayyoubzadeh S M, Ebrahimi M, Rostam Niakan Kalhor S, Abooei A H, Gholamzadeh M et al . Prediction of COVID-19 Patients’ Survival by Deep Learning Approaches. Med J Islam Repub Iran 2022; 36 (1) :1099-1106
URL: http://mjiri.iums.ac.ir/article-1-8143-en.html
URL: http://mjiri.iums.ac.ir/article-1-8143-en.html
Moloud Taheriyan 
, Seyed Mehdi Ayyoubzadeh , Mehdi Ebrahimi , Sharareh Rostam Niakan Kalhor , Amir Hossien Abooei , Marsa Gholamzadeh , Seyed Mohammad Ayyoubzadeh
, Seyed Mehdi Ayyoubzadeh , Mehdi Ebrahimi , Sharareh Rostam Niakan Kalhor , Amir Hossien Abooei , Marsa Gholamzadeh , Seyed Mohammad Ayyoubzadeh
Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran , smayyoubzadeh@sina.tums.ac.ir
Abstract: (1219 Views)
Background: Despite many studies done to predict severe coronavirus 2019 (COVID-19) patients, there is no applicable clinical prediction model to predict and distinguish severe patients early. Based on laboratory and demographic data, we have developed and validated a deep learning model to predict survival and assist in the triage of COVID-19 patients in the early stages.
Methods: This retrospective study developed a survival prediction model based on the deep learning method using demographic and laboratory data. The database consisted of data from 487 patients with COVID-19 diagnosed by the reverse transcription-polymerase chain reaction test and admitted to Imam Khomeini hospital affiliated to Tehran University of Medical Sciences from February 21, 2020, to June 24, 2020.
Results: The developed model achieved an area under the curve (AUC) of 0.96 for survival prediction. The results demonstrated the developed model provided high precision (0.95, 0.93), recall (0.90,0.97), and F1-score (0.93,0.95) for low- and high-risk groups.
Conclusion: The developed model is a deep learning-based, data-driven prediction tool that can predict the survival of COVID-19 patients with an AUC of 0.96. This model helps classify admitted patients into low-risk and high-risk groups and helps triage patients in the early stages.
Methods: This retrospective study developed a survival prediction model based on the deep learning method using demographic and laboratory data. The database consisted of data from 487 patients with COVID-19 diagnosed by the reverse transcription-polymerase chain reaction test and admitted to Imam Khomeini hospital affiliated to Tehran University of Medical Sciences from February 21, 2020, to June 24, 2020.
Results: The developed model achieved an area under the curve (AUC) of 0.96 for survival prediction. The results demonstrated the developed model provided high precision (0.95, 0.93), recall (0.90,0.97), and F1-score (0.93,0.95) for low- and high-risk groups.
Conclusion: The developed model is a deep learning-based, data-driven prediction tool that can predict the survival of COVID-19 patients with an AUC of 0.96. This model helps classify admitted patients into low-risk and high-risk groups and helps triage patients in the early stages.
Type of Study: Original Research |
Subject:
COVID 19
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