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Current Medical Imaging

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ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

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Review Article

Coronavirus-related Disease Pandemic: A Review on Machine Learning Approaches and Treatment Trials on Diagnosed Population for Future Clinical Decision Support

Author(s): Reyana A.* and Sandeep Kautish

Volume 18, Issue 2, 2022

Published on: 14 April, 2021

Article ID: e140421192833 Pages: 9

DOI: 10.2174/1573405617666210414101941

Price: $65

Abstract

Objective: Coronavirus-related disease, a deadly illness, has raised public health issues worldwide. The majority of individuals infected are multiplying. The government is taking aggressive steps to quarantine people, people exposed to infection, and clinical trials for treatment. Subsequently recommends critical care for the aged, children, and health-care personnel. While machine learning methods have been previously used to augment clinical decisions, there is now a demand for “Emergency ML.” With rapidly growing datasets, there also remain important considerations when developing and validating ML models.

Methods: This paper reviews the recent study that applies machine-learning technology addressing Corona virus-related disease issues' challenges in different perspectives. The report also discusses various treatment trials and procedures on Corona virus-related disease infected patients providing insights to physicians and the public on the current treatment challenges.

Results: The paper provides the individual with insights into certain precautions to prevent and control the spread of this deadly disease.

Conclusion: This review highlights the utility of evidence-based machine learning prediction tools in several clinical settings, and how similar models can be deployed during the Corona virus-related disease pandemic to guide hospital frontlines and health-care administrators to make informed decisions about patient care and managing hospital volume. Further, the clinical trials conducted so far for infected patients with Corona virus-related disease addresses their results to improve community alertness from the viewpoint of a well-known saying, “prevention is always better.”

Keywords: Corona virus-related disease, clinical, pandemic, prevention, complications, machine learning.

Graphical Abstract

[1]
Covid CDC, Team R. Severe outcomes among patients with coronavirus disease 2019 (COVID-19)-United States. MMWR Morb Mortal Wkly Rep 2020; 69(12): 343-6.
[PMID: 32214079]
[2]
Bedford J, Enria D, Giesecke J, et al. COVID-19: towards controlling of a pandemic. Lancet 2020; 395(10229): 1015-8.
[http://dx.doi.org/10.1016/S0140-6736(20)30673-5] [PMID: 32197103]
[3]
World Health Organization. Coronavirus disease 2019 (COVID-19): situation report 2020; 82: 1-13.
[4]
Touret F, de Lamballerie X. Of chloroquine and COVID-19. Antiviral Res 2020; 177: 104762.
[http://dx.doi.org/10.1016/j.antiviral.2020.104762] [PMID: 32147496]
[5]
Remuzzi A, Remuzzi G. COVID-19 and Italy: what next? Lancet 2020; 395(10231): 1225-8.
[http://dx.doi.org/10.1016/S0140-6736(20)30627-9] [PMID: 32178769]
[6]
Shi Y, Wang Y, Shao C, et al. COVID-19 infection: the perspectives on immune responses Cell Death Differ 2020; 27(5): 1451-4.
[7]
Fang Y, Zhang H, Xie J, et al. Sensitivity of chest CT for COVID-19: Comparison to RT-PCR. Radiology 2020; 296(2): E115-7.
[http://dx.doi.org/10.1148/radiol.2020200432] [PMID: 32073353]
[8]
Xu X, Han M, Li T, et al. Effective treatment of severe COVID-19 patients with tocilizumab. Proc Natl Acad Sci USA 2020; 117(20): 10970-5.
[http://dx.doi.org/10.1073/pnas.2005615117] [PMID: 32350134]
[9]
Vaduganathan M, Vardeny O, Michel T, McMurray JJV, Pfeffer MA, Solomon SD. Renin–angiotensin–aldosterone system inhibitors in patients with Covid-19. N Engl J Med 2020; 382(17): 1653-9.
[http://dx.doi.org/10.1056/NEJMsr2005760] [PMID: 32227760]
[10]
Lurie N, Saville M, Hatchett R, Halton J. Developing Covid-19 vaccines at pandemic speed. N Engl J Med 2020; 382(21): 1969-73.
[http://dx.doi.org/10.1056/NEJMp2005630] [PMID: 32227757]
[11]
Rothan HA, Byrareddy SN. The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak. J Autoimmun 2020; 109: 102433.
[http://dx.doi.org/10.1016/j.jaut.2020.102433] [PMID: 32113704]
[12]
Nour M, Cömert Z, Polat K. A novel medical diagnosis model for COVID-19 infection detection based on deep features and bayesian optimization. Appl Soft Comput 2020; 97: 106580.
[http://dx.doi.org/10.1016/j.asoc.2020.106580] [PMID: 32837453]
[13]
Yadav M, Perumal M, Srinivas M. Analysis on novel coronavirus (COVID-19) using machine learning methods. Chaos Solitons Fractals 2020; 139: 110050.
[http://dx.doi.org/10.1016/j.chaos.2020.110050] [PMID: 32834604]
[14]
Wang P, Zheng X, Li J, Zhu B. Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics. Chaos Solitons Fractals 2020; 139: 110058.
[http://dx.doi.org/10.1016/j.chaos.2020.110058] [PMID: 32834611]
[15]
Cole MA, Elliott RJR, Liu B. The impact of the Wuhan Covid-19 lockdown on air pollution and health: a machine learning and augmented synthetic control approach. Environ Resour Econ (Dordr) 2020; 76(4): 1-28.
[http://dx.doi.org/10.1007/s10640-020-00483-4] [PMID: 32836865]
[16]
Samuel J, Ali GG, Rahman M, Esawi E, Samuel Y. Covid-19 public sentiment insights and machine learning for tweets classification. Information 2020; 11(6): 314.
[http://dx.doi.org/10.3390/info11060314]
[17]
Randhawa GS, Soltysiak MPM, El Roz H, de Souza CPE, Hill KA, Kari L. Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study. PLoS One 2020; 15(4): e0232391.
[http://dx.doi.org/10.1371/journal.pone.0232391] [PMID: 32330208]
[18]
Lalmuanawma S, Hussain J, Chhakchhuak L. Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review. Chaos Solitons Fractals 2020; 139: 110059.
[http://dx.doi.org/10.1016/j.chaos.2020.110059] [PMID: 32834612]
[19]
Bastard P, Rosen L B, Zhang Q, Michailidis E, Hoffmann H H, Zhang Y, et al. Science 2020; 370: 6515.
[http://dx.doi.org/10.1126/science.abd4585]
[20]
CDC COVID-19 Response Team. Characteristics of Health Care Personnel with COVID-19-United States. February 12-April; 2020; pp. 69(15): 477-81. MMWR 2020
[21]
Struyf T, Deeks JJ, Dinnes J, et al. Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19 disease. Cochrane Database of Systematic Reviews 2020; 7: CD013665.
[22]
Jeyanathan M, Afkhami S, Smaill F, Miller MS, Lichty BD, Xing Z. Immunological considerations for COVID-19 vaccine strategies. Nat Rev Immunol 2020; 20(10): 615-32.
[http://dx.doi.org/10.1038/s41577-020-00434-6] [PMID: 32887954]
[23]
Zhao D, Yao F, Wang L, et al. A comparative study on the clinical features of COVID-19 pneumonia to other pneumonias. Clin Infect Dis 2020; 71(15): 756-61.
[http://dx.doi.org/10.1093/cid/ciaa247] [PMID: 32161968]
[24]
Zhang Q, Bastard P, Liu Z, et al. Inborn errors of type I IFN immunity in patients with life-threatening COVID-19. Science 2020; 370(6515): eabd4570.
[http://dx.doi.org/10.1126/science.abc5441]
[25]
Poyiadji N, Shahin G, Noujaim D, Stone M, Patel S, Griffith B. COVID-19–associated acute hemorrhagic necrotizing encephalopathy: CT and MRI features. Radiology 2020; 296: E119-20.
[http://dx.doi.org/10.1148/radiol.2020201187]
[26]
Hollander JE, Carr BG. Virtually perfect? Telemedicine for COVID-19. N Engl J Med 2020; 382(18): 1679-81.
[http://dx.doi.org/10.1056/NEJMp2003539] [PMID: 32160451]
[27]
Fauci AS, Lane HC, Redfield RR. Covid-19—navigating the uncharted. N Engl J Med 2020; 382: 1268-9.
[28]
Klok FA, Kruip MJHA, Van der Meer NJM, et al. Incidence of thrombotic complications in critically ill ICU patients with COVID-19. Thromb Res 2020; 191: 145-7.
[http://dx.doi.org/10.1016/j.thromres.2020.04.013]
[29]
Gandhi RT, Lynch JB, Del Rio C. Mild or moderate COVID-19. N Engl J Med 2020; 383(18): 1757-66.
[http://dx.doi.org/10.1056/NEJMcp2009249] [PMID: 32329974]
[30]
Dexamethasone in hospitalized patients with Covid-19-preliminary report. N Engl J Med 2020; 69(15): 477-81.
[31]
Grein J, Ohmagari N, Shin D, et al. Compassionate use of remdesivir for patients with severe Covid-19. N Engl J Med 2020; 382(24): 2327-36.
[http://dx.doi.org/10.1056/NEJMoa2007016] [PMID: 32275812]
[32]
Cao B, Wang Y, Wen D, et al. A trial of lopinavir–ritonavir in adults hospitalized with severe Covid-19. N Engl J Med 2020; 382(19): 1787-99.
[http://dx.doi.org/10.1056/NEJMoa2001282] [PMID: 32187464]
[33]
Cao X. COVID-19: immunopathology and its implications for therapy. Nat Rev Immunol 2020; 20(5): 269-70.
[http://dx.doi.org/10.1038/s41577-020-0308-3] [PMID: 32273594]
[34]
Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis 2020; 20(5): 533-4.
[http://dx.doi.org/10.1016/S1473-3099(20)30120-1] [PMID: 32087114]
[35]
Malki Z, Atlam ES, Hassanien AE, Dagnew G, Elhosseini MA, Gad I. Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches. Chaos Solitons Fractals 2020; 138: 110137.
[http://dx.doi.org/10.1016/j.chaos.2020.110137] [PMID: 32834583]
[36]
Loey M, Manogaran G, Taha MHN, Khalifa NEM. A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic. Measurement (Lond) 2021; 167: 108288.
[http://dx.doi.org/10.1016/j.measurement.2020.108288] [PMID: 32834324]

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