Profile
International Journal of Computer & Software Engineering Volume 6 (2021), Article ID 6:IJCSE-173, 9 pages
https://doi.org/10.15344/2456-4451/2021/173
Research Article
COVID-19 Severity Score Using Machine Learning

Naser Zaeri

Faculty of Computer Studies, Arab Open University, Kuwait
Dr Naser Zaeri, Faculty of Computer Studies, Arab Open University, Kuwait; E-mail: n.zaeri@aou.edu.kw
27 October 2021; 22 November 2021; 24 November 2021
Zaeri N (2021) COVID-19 Severity Score Using Machine Learning. Int J Comput Softw Eng 6: 173. doi: https://doi.org/10.15344/2456-4451/2021/173
The project was fully funded by KFAS under project code: PN20- 13NH-03.

References

  1. Ng MY, Lee EYP, Yang J, Yang F, Li X, et al. (2020) Imaging profile of the covid-19 infection: Radiologic findings and literature review. Radiol Cardiothorac Imaging 13: e200034. [CrossRef] [Google Scholar] [PubMed]
  2. Pham QV, Nguyen DC, Huynh-The T, Hwang WJ, Pathirana PN, et al. (2020) Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts. [CrossRef] [Google Scholar]
  3. Wang L, Lin ZQ, Wong A (2020) COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases. Chest Radiography Images. [CrossRef] [Google Scholar]
  4. Fan DP, Zhou T, Ji GP, Zhou Y, Chen G, et al. (2002) Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images. IEEE Transactions on Medical Imaging 39: 2626-2637. [CrossRef] [Google Scholar]
  5. Feng Y, Ling Y, Bai T, Xie Y, Huang J, et al. (2020) COVID-19 with different severities: A multicenter study of clinical features. Am J Respir Crit Care Med 201: 1380-1388. [CrossRef] [Google Scholar] [PubMed]
  6. Cohen JP, Dao L, Morrison P, Roth K, Bengio Y, et al. (2020) Predicting covid-19 pneumonia severity on chest x-ray with deep learning. Cureus 12: e9448. [CrossRef] [Google Scholar] [PubMed]
  7. Zhu J, Shen B, Abbasi A, Hoshmand-Kochi M, Li H, et al. (2020) Deep transfer learning artificial intelligence accurately stages covid-19 lung disease severity on portable chest radiographs. PloS One 15: e0236621. [CrossRef] [Google Scholar] [PubMed]
  8. Signoroni A, Savardi M, Benini S, Adami N, Leonardi R, et al. (2020) End-to-end learning for semiquantitative rating of covid-19 severity on chest x-rays. [Google Scholar]
  9. Duchesne S, Gourdeau D, Archambault P, Chartrand-Lefebvre C, Dieumegarde L, et al. (2020) Tracking and predicting covid-19 radiological trajectory using deep learning on chest x-rays: Initial accuracy testing. medRxiv. [CrossRef] [Google Scholar]
  10. Yan L, Zhang HT, Xiao Y, Wang M, Guo Y, 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. medRxiv. [CrossRef] [Google Scholar]
  11. Jiang X, Coee M, Bari A, Wang J, Jiang X, et al. (2020) Towards an Artificial Intelligence Framework for Data-Driven Prediction of Coronavirus Clinical Severity. Computers, Materials and Continua 63: 537-551. [CrossRef] [Google Scholar]
  12. Sujatha R, Chatterjee JM, Hassanien AE (2020) A machine learning forecasting model for COVID-19 pandemic in India. Stoch Environ Res Risk Assess 34: 959-972. [CrossRef] [Google Scholar] [PubMed]
  13. Mader C, Bernatz S, Michalik S, Koch V, Martin SS, et al. (2021) Quantification of COVID-19 Opacities on Chest CT - Evaluation of a Fully Automatic AI-approach to Noninvasively Differentiate Critical Versus Noncritical Patients. Acad Radiol. [CrossRef] [Google Scholar] [PubMed]
  14. Lalmuanawma S, Hussain J, Chhakchhuak L (2020) Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review. Chaos Solitons and Fractals 139: 110059. [CrossRef] [Google Scholar] [PubMed]
  15. Sun L, Liu G, Song F, Shi N, Liu F, et al. (2020) Combination of four clinical indicators predicts the severe/critical symptom of patients infected COVID-19. J Clin Virol 128: 104431. [CrossRef] [Google Scholar] [PubMed]
  16. Tang Z, Zhao W, Xie X, Zhong Z, Shi F, et al. (2003) Severity assessment of coronavirus disease 2019 (COVID-19) using quantitative features from chest CT images. Phys Med Biol 66: 035015. [CrossRef] [Google Scholar] [PubMed]
  17. Greenspan H, Estépar RSJ, Niessen WJ, Siegel E, Nielsen M, et al. (2020) Position paper on COVID-19 imaging and AI: From the clinical needs and technological challenges to initial AI solutions at the lab and national level towards a new era for AI in healthcare. Med Image Anal 66: 101800. [CrossRef] [Google Scholar] [PubMed]
  18. Dong D, Tang Z, Wang S, Hui H, Gong L, et al. (2020) The role of imaging in the detection and management of COVID-19: a review. IEEE Rev Biomed Eng 14: 16-29. [CrossRef] [Google Scholar] [PubMed]
  19. Zhu J, Shen B, Abbasi A, Hoshmand-Kochi M, Li H, et al. (2020) Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs. PLoS One 15: e0236621. [CrossRef] [Google Scholar] [PubMed]
  20. Wismüller A, Stockmaster L (2020) Tracking Results and Utilization of Artificial Intelligence (tru-AI) in Radiology: Early-Stage COVID-19 Pandemic Observations. [Google Scholar]
  21. Lessmann N, Sánchez CI, Beenen L, Boulogne LH, Brink M, et al. (2020) Automated Assessment of CO-RADS and Chest CT Severity Scores in Patients with Suspected COVID-19 Using Artificial Intelligence. Radiology 30: 202439. [CrossRef] [Google Scholar] [PubMed]
  22. Chassagnon G, Vakalopoulou M, Battistella E, Christodoulidis S, Hoang-Thi TN, et al. (2021) AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia,” Med Image Anal 67: 101860. [CrossRef] [Google Scholar] [PubMed]
  23. Ghesu FC, Georgescu B, Zheng Y, Gribic S, Maier A, et al. (2019) Multi-scale deep reinforcement learning for real-time 3D-landmark detection in CT scans. IEEE Trans Pattern Anal Mach Intell 41: 176-189. [CrossRef] [Google Scholar]
  24. Fang X, Kruger U, Homayounieh F, Chao H, Zhang J, et al. (2021) Association of AI quantified COVID-19 chest CT and patient outcome. Int J Comput Assist Radiol Surg 16: 435-445. [CrossRef] [Google Scholar] [PubMed]
  25. Suri JS, Puvvula A, Biswas M, Majhail M, Saba L, et al. (2020) COVID-19 pathways for brain and heart injury in comorbidity patients: A role of medical imaging and artificial intelligence-based COVID severity classification: A review. Comput Biol Med 124: 103960. [CrossRef] [Google Scholar] [PubMed]
  26. Jakhar D, Kaur I, Kaul S (2020) Art of performing dermoscopy during the times of coronavirus disease (COVID-19): simple change in approach can save the day! J Eur Acad Dermatol Venereol 34: e242-e244. [CrossRef] [Google Scholar] [PubMed]
  27. Cieszanowski A, Czekajska E, Gizycka B, Gruszczynska K, Podgorska J, et al. (2020) Management of patients with COVID-19 in radiology departments, and indications regarding imaging studies-recommendations of the Polish Medical Society of Radiology. Pol J Radiol 85: e209. [CrossRef] [Google Scholar] [PubMed]
  28. Jamthikar A, Gupta D, Khanna NN, Saba L, Araki T, et al. (2019) A low-cost machine learning-based cardiovascular/stroke risk assessment system: integration of conventional factors with image phenotypes. Cardiovasc Diagn Ther 9: 420. [CrossRef] [Google Scholar] [PubMed]
  29. Lassau N, Ammari S, Chouzenoux E, Gortais H, Herent P, et al. (2021) Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients. Nat Commun 12: 634. [CrossRef] [Google Scholar] [PubMed]