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Mortazavi, S A R, Mortazavi, S M J, Parsaei, H. (1399). COVID-19 Pandemic: How to Use Artificial Intelligence to Choose Non-Vulnerable Workers for Positions with the Highest Possible Levels of Exposure to the Novel Coronavirus. سامانه مدیریت نشریات علمی, 10(3), 383-386. doi: 10.31661/jbpe.v0i0.2004-1106
S A R Mortazavi; S M J Mortazavi; H Parsaei. "COVID-19 Pandemic: How to Use Artificial Intelligence to Choose Non-Vulnerable Workers for Positions with the Highest Possible Levels of Exposure to the Novel Coronavirus". سامانه مدیریت نشریات علمی, 10, 3, 1399, 383-386. doi: 10.31661/jbpe.v0i0.2004-1106
Mortazavi, S A R, Mortazavi, S M J, Parsaei, H. (1399). 'COVID-19 Pandemic: How to Use Artificial Intelligence to Choose Non-Vulnerable Workers for Positions with the Highest Possible Levels of Exposure to the Novel Coronavirus', سامانه مدیریت نشریات علمی, 10(3), pp. 383-386. doi: 10.31661/jbpe.v0i0.2004-1106
Mortazavi, S A R, Mortazavi, S M J, Parsaei, H. COVID-19 Pandemic: How to Use Artificial Intelligence to Choose Non-Vulnerable Workers for Positions with the Highest Possible Levels of Exposure to the Novel Coronavirus. سامانه مدیریت نشریات علمی, 1399; 10(3): 383-386. doi: 10.31661/jbpe.v0i0.2004-1106

COVID-19 Pandemic: How to Use Artificial Intelligence to Choose Non-Vulnerable Workers for Positions with the Highest Possible Levels of Exposure to the Novel Coronavirus

Journal of Biomedical Physics and Engineering
مقاله 16، دوره 10، شماره 3، شهریور 2020، صفحه 383-386    اصل مقاله (340.47 K)
نوع مقاله: Hypothesis
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.2004-1106
نویسندگان
S A R Mortazavi1؛ S M J Mortazavi2؛ H Parsaei* 2
1MD, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
2PhD, Medical Physics and Engineering Department, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
چکیده
The COVID-19 pandemic has affected people globally; nowadays several countries are facing a major change in daily life due to universal quarantining, closed schools, social isolation, and shelter-in-place orders. In addition, this pandemic caused an economic crisis. International Labour Organization (ILO) reported that not only COVID-19 is a serious threat to public health but also the economic and social disruption of this crisis threatens the long-term livelihoods and wellbeing of millions of people. In this short paper, we introduce our hypothesis on using artificial intelligence (AI) technology to predict which employees are most vulnerable to the infections caused by the novel coronavirus (SARS-CoV-2). Such a system can be used in a wide variety of work places such as libraries, banks, drugstores, and hotel receptions to reduce the risk of severe infections in employees as well as to ensure the safety of labor force and the sustainability of businesses and jobs.
کلیدواژه‌ها
COVID-19؛ Coronavirus؛ Machine Learning؛ Prediction Model؛ Classification
سایر فایل های مرتبط با مقاله
مراجع
  1. Pan A, Liu L, Wang C, Guo H, Hao X, Wang Q, Huang J, He N, Yu H, Lin X, Wei S. Association of public health interventions with the epidemiology of the COVID-19 outbreak in Wuhan, China. JAMA. 2020. doi: 10.1001/jama.2020.6130.
  2. Shanafelt T, Ripp J, Trockel M. Understanding and addressing sources of anxiety among health care professionals during the COVID-19 pandemic. JAMA. 2020.
  3. Yang Y, Lu Q, Liu M, Wang Y, Zhang A, Jalali N, Dean N, Longini I, Halloran ME, Xu B, Zhang X. Epidemiological and clinical features of the 2019 novel coronavirus outbreak in China. MedRxiv. 2020. doi: 10.1101/2020.02.10.20021675.
  4. Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, Wang B, Xiang H, Cheng Z, Xiong Y, Zhao Y. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan, China. JAMA. 2020;323(11):1061-9. doi: 10.1001/jama.2020.1585. PubMed PMID: 32031570. PubMed PMCID: PMC7042881.
  5. Grasselli G, Zangrillo A, Zanella A, Antonelli M, Cabrini L, Castelli A, Cereda D, Coluccello A, Foti G, Fumagalli R, Iotti G. Baseline characteristics and outcomes of 1591 patients infected with SARS-CoV-2 admitted to ICUs of the Lombardy region, Italy. JAMA. 2020;323(16):1574-1581. doi: 10.1001/jama.2020.5394.
  6. International Labour Organization. COVID-19: Protecting workers in the workplace: Almost 25 million jobs could be lost worldwide as a result of COVID-19. ILO; 2020.
  7. Wynants L, Van Calster B, Bonten MMJ, Collins GS, Debray TP, De Vos M, Haller MC, Heinze G, Moons KG, Riley RD, Schuit E. Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal. BMJ. 2020;369.
  8. Wim N. Artificial Intelligence against COVID-19: An Early Review. IZA DP No. 13110; Germany: IZA – Institute of Labor Economics; 2020.
  9. Shi F, Wang J, Shi J, Wu Z, Wang Q, Tang Z, et al. Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19. IEEE Reviews in Biomedical Engineering. 2020. doi: 10.1109/RBME.2020.2987975.
  10. Pourhomayoun M, Shakibi M. Predicting Mortality Risk in Patients with COVID-19 Using Artificial Intelligence to Help Medical Decision-Making. MedRxiv. 2020. doi: 10.1101/2020.03.30.20047308.
  11. Guyon I, Elisseeff A. An introduction to variable and feature selection. Journal of machine learning research. 2003;3:1157-82.
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