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Cited by 87COVID-19 diagnosis using AIArtificial Intelligence in Healthcare and EducationHealthcare during COVID-19 Pandemic

The DOI matches its Crossref record; 309 of the 309 references it cites were found in a registry or an index.

A systematic review on AI/ML approaches against COVID-19 outbreak

Open access
Dogan, Onur, Tiwari, Sanju, Jabbar, M. A., Guggari, Shankru·2021·Complex & Intelligent Systems
DOI 10.1007/s40747-021-00424-8PMC PMC8256231
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Abstract

A pandemic disease, COVID-19, has caused trouble worldwide by infecting millions of people. The studies that apply artificial intelligence (AI) and machine learning (ML) methods for various purposes against the COVID-19 outbreak have increased because of their significant advantages. Although AI/ML applications provide satisfactory solutions to COVID-19 disease, these solutions can have a wide diversity. This increase in the number of AI/ML studies and diversity in solutions can confuse deciding which AI/ML technique is suitable for which COVID-19 purposes. Because there is no comprehensive review study, this study systematically analyzes and summarizes related studies. A research methodology has been proposed to conduct the systematic literature review for framing the research questions, searching criteria and relevant data extraction. Finally, 264 studies were taken into account after following inclusion and exclusion criteria. This research can be regarded as a key element for epidemic and transmission prediction, diagnosis and detection, and drug/vaccine development. Six research questions are explored with 50 AI/ML approaches in COVID-19, 8 AI/ML methods for patient outcome prediction, 14 AI/ML techniques in disease predictions, along with five AI/ML methods for risk assessment of COVID-19. It also covers AI/ML method in drug development, vaccines for COVID-19, models in COVID-19, datasets and their usage and dataset applications with AI/ML.

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Crossref · 10.1007/s40747-021-00424-8

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309 of 309 references verified0 citation errors · 0 not checkable

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Fig. 1 Systematic literature review flowchart
Fig. 1 Systematic literature review flowchartCC BY · PubMed Central
Fig. 2 Result of the study selection process
Fig. 2 Result of the study selection processCC BY · PubMed Central
Fig. 3 AI/ML approaches in COVID-19
Fig. 3 AI/ML approaches in COVID-19CC BY · PubMed Central
Fig. 4 Objectives of AI/ML approaches in COVID-19
Fig. 4 Objectives of AI/ML approaches in COVID-19CC BY · PubMed Central
Fig. 5 Drugs and vaccines for COVID-19
Fig. 5 Drugs and vaccines for COVID-19CC BY · PubMed Central

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Citations

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Research works printed but not linked: 26

  1. Manapure P, Likhar K, Kosare H (2020) Detecting COVID-19 in X-ray images with keras, tensor flow, and deep learning. Artif Comput Intell 2(3):1–6
  2. Asif S, Wenhui Y (2020) Automatic detection of COVID-19 using X-ray images with deep convolutional neural networks and machine learning, preprint on webpage at https://doi.org/10.1101/2020.05.01.20088211v2 · DOI
  3. Zhu H, Guo Q, Li M, Wang C, Fang Z, Wang P, Tan J, Wu S, Xiao Y (2020) Host and infectivity prediction of Wuhan 2019 novel coronavirus using deep learning algorithm, preprint on webpage at https://doi.org/10.1101/2020.01.21.914044v4 · DOI
  4. Jamil M, Hussain I et al (2020) Automatic detection of COVID-19 infection from chest x-ray using deep learning, preprint on webpage at https://doi.org/10.1101/2020.05.10.20097063v1 · DOI
  5. Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, Bai J, Lu Y, Fang Z, Song Q et al (2020) Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT, preprint on webpage at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233473/
  6. Dang Q, Miao R, Yong L (2020) COVID-19 in shang hai: it is worth learning from the successful experience in preventing and controlling the overseas epidemic situation, preprint on webpage at https://doi.org/10.1101/2020.05.13.20100164v1 · DOI
  7. Rui M, Qi D, Yong L (2020) A sparse gaussian network model for prediction the growth trend of COVID-19 overseas import case: When can Hong Kong lift the international traffic blockad? Preprint on webpage at https://doi.org/10.1101/2020.05.13.20099978v1 · DOI
  8. Wu J, Zhang P, Zhang L, Meng W, Li J, Tong C, Li Y, Cai J, Yang Z, Zhu J et al (2020) Rapid and accurate identification of COVID-19 infection through machine learning based on clinical available blood test results, preprint on webpage at https://doi.org/10.1101/2020.04.02.20051136v1 · DOI
  9. Feng C, Huang Z, Wang L, Chen X, Zhai Y, Zhu F, Chen H, Wang Y, Su X, Huang S et al (2020) A novel triage tool of artificial intelligence assisted diagnosis aid system for suspected COVID-19 pneumonia in fever clinics, preprint on webpage at https://doi.org/10.1101/2020.03.19.20039099v1 · DOI
  10. Yan L, Zhang H-T, Xiao Y, Wang M, Guo Y, Sun C, Tang X, Jing L, Li S, Zhang M et al (2020) Prediction of criticality in patients with severe COVID-19 infection using three clinical features: a machine learning-based prognostic model with clinical data in Wuhan, preprint on webpage at https://doi.org/10.1101/2020.02.27.20028027v2 · DOI
  11. Linda W (2020) A tailored deep convolutional neural network design for detection of COVID-19 cases from chest radiography images. J Netw Comput Appl 20:1–12
  12. Bandyopadhyay SK, Dutta S (2020) Machine learning approach for confirmation of COVID-19 cases: positive, negative, death and release, preprint on webpage at https://doi.org/10.1101/2020.03.25.20043505v1 · DOI
  13. Hollister M (2020) AI can help with the COVID-19 crisis-but the right human input is key. World Econ Forum 30:1–4
  14. Bai X, Fang C, Zhou Y, Bai S, Liu Z, Xia L, Chen Q, Xu Y, Xia T, Gong S et al (2020) Predicting COVID-19 malignant progression with AI techniques, preprint on webpage at https://doi.org/10.1101/2020.03.20.20037325v2 · DOI
  15. Zheng C, Deng X, Fu Q, Zhou Q, Feng J, Ma H, Liu W, Wang X (2020) Deep learning-based detection for COVID-19 from chest CT using weak label, preprint on webpage at https://doi.org/10.1101/2020.03.12.20027185v2 · DOI
  16. Sarkar J, Chakrabarti P (2020) A machine learning model reveals older age and delayed hospitalization as predictors of mortality in patients with COVID-19, preprint on webpage at https://doi.org/10.1101/2020.03.25.20043331v1 · DOI
  17. Shin B, Park S, Kang K, Ho JC (2019) Self-attention based molecule representation for predicting drug-target interaction. In: Machine learning for healthcare conference. PMLR, pp 230–248
  18. Singh R, Singh R, Bhatia A (2018) Sentiment analysis using machine learning technique to predict outbreaks and epidemics. Int J Adv Sci Res 3(2):19–24
  19. Ranjan R (2020) Predictions for COVID-19 outbreak in India using epidemiological models, preprint on webpage at https://doi.org/10.1101/2020.04.02.20051466v1 · DOI
  20. DAS A, Mishra S, Gopalan SS (2020) Predicting community mortality risk due to COVID-19 using machine learning and development of a prediction tool, preprint on webpage at https://doi.org/10.1101/2020.04.27.20081794v2 · DOI
  21. Gupta R, Pal SK, Pandey G (2020) A comprehensive analysis of COVID-19 outbreak situation in India, preprint on webpage at https://doi.org/10.1101/2020.04.08.20058347v2 · DOI
  22. Rai B, Shukla A, Dwivedi LK (2020) COVID-19 in India: predictions, reproduction number and public health preparedness, preprint on webpage at https://doi.org/10.1101/2020.04.09.20059261v1 · DOI
  23. Zhao J, Zhang Y, He X, Xie P (2020) COVID-CT-dataset: a CT scan dataset about COVID-19, preprint on webpage at https://covid-19.conacyt.mx/jspui/handle/1000/4157
  24. Lin ZQ, Shafiee M, Bochkarev S, Jules MS, Wang X, Wong A (2019) Explaining with impact: a machine-centric strategy to quantify the performance of explain ability algorithms, preprint on webpage at https://doi.org/10.1101/2020.05.10.20097063v1 · DOI
  25. Lai C-C, Hsu C-Y, Jen H-H, Yen M-F, Chan C-C, Chen H-H (2020) Bayesian approach for modelling the dynamic of COVID-19 outbreak on the diamond princess cruise ship, preprint on webpage at https://doi.org/10.1101/2020.06.21.20136465v1 · DOI
  26. Tindale L, Coombe M, Stockdale JE, Garlock E, Lau WYV, Saraswat M, Lee Y-HB, Zhang L, Chen D, Wallinga J et al (2020) Transmission interval estimates suggest pre-symptomatic spread of COVID-19, preprint on webpage at https://doi.org/10.1101/2020.03.03.20029983v1 · DOI

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Metadata

Authors
Dogan, O., Tiwari, S., Jabbar, M. A., & Guggari, S.
Affiliations
  • Autonomous University of Tamaulipas (MX)
  • Vardhaman College of Engineering (IN)
  • B.M.S. College of Engineering (IN)
ORCID
Venue
Complex & Intelligent Systems
Publisher
Springer Science and Business Media LLC
Published
2021-07-05
Volume
7
Issue
5
Pages
2655-2678
ISSN
  • 2198-6053 (ISSN-L)
  • 2199-4536
DOI
10.1007/s40747-021-00424-8
PMC
PMC8256231
OpenAlex
W3180100733
Cited by
87
References
290
Citations by year
2026: 4 · 2025: 11 · 2024: 15 · 2023: 25 · 2022: 26 · 2021: 6
FWCI
2.94
Type
journal article
Language
en
Open access
Licence
Locations
  • Complex & Intelligent Systems (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Padua Research Archive (University of Padova) (submittedVersion, cc-by)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Information Systems › Healthcare during COVID-19 Pandemic
Keywords
COVID-19 pandemic, artificial intelligence, machine learning, epidemic forecasting, COVID-19 diagnosis, COVID-19 detection, drug development, vaccine development, clinical outcome prediction, risk assessment, COVID-19 datasets

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