Volume 15, Issue 3 ( Special Issue (AI in Medicine) - August 2023 2023)                   Iranian Journal of Blood and Cancer 2023, 15(3): 93-111 | Back to browse issues page

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Ghaderzadeh M, Asadi F, Ramezan Ghorbani N, Almasi S, Taami T. Toward artificial intelligence (AI) applications in the determination of COVID-19 infection severity: considering AI as a disease control strategy in future pandemics. Iranian Journal of Blood and Cancer 2023; 15 (3) :93-111
URL: http://ijbc.ir/article-1-1382-en.html
Toward artificial intelligence (AI) applications in the determination of COVID-19 infection severity: considering AI as a disease control strategy in future pandemics
Mustafa Ghaderzadeh1 , Farkhondh Asadi * 2, Nahid Ramezan Ghorbani3 , Sohrab Almasi4 , Tania Taami5
1- Department of Artificial Intelligence, Smart University of Medical Science, Tehran, Iran
2- Health Information Management, Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran , Asadifar@sbmu.ac.ir
3- Department of Development & Coordination Scientific Information and Publications, Deputy of Research & Technology, Ministry of Health & Medical Education, Tehran, Iran.
4- Health Information Management, Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
5- Department of Computer Science, Tallahassee, FL, USA
Abstract:   (363 Views)

Background: To guarantees patient survival and reduce the consumption of pharmaceutical and medical resources, an accurate diagnosis and assessment of COVID-19 severity are crucial. Since the outbreak of the pandemic, researchers have evaluated, identified, and predicted the severity of COVID-19 using a range of AI techniques. Due to the lack of a systematic review in the field of analysis of these studies, the present research rigorously reviewed all the pertinent literature.
Methods: Between December 1, 2019, and January 1, 2022, 762 articles were found by searching the PubMed, Scopus, Web of Science, and Scholar databases using the search method. 34 papers were chosen from this group as the research community's representatives using inclusion and exclusion criteria.
Results: By looking at the machine learning approach used in this research, it can be seen that XGBoost and SVM algorithms were more prevalent and effective in identifying the severity of the condition, according to the results of the data analysis used in this study. A set of impressive features, including clinical, demographic, laboratory, and serology data, was used to calculate the severity of COVID-19 using ML algorithms. By calculating the performance metric, it can be concluded that the ML methods had high sensitivity and specificity in determining the severity of COVID-19.
Conclusion: Deep learning methods, as cutting-edge methods, have a significant tangible capacity for providing an accurate and efficient intelligent system for detecting and estimating the severity of COVID-19. It is recommended that in the future or other variants of COVID-19 epidemics, AI-based systems in conjunction with IoT, cloud storage, and 5G technologies be used to remove geographical problems in the rapid estimation of disease severity, immediate epidemic control before the pandemic, epidemic management, and lower treatment costs

Keywords: Artificial Intelligence, Pandemic, COVID-19, XGBoost, SVM, IoT, 5G
Full-Text [PDF 5805 kb]   (209 Downloads)    
: Review Article | Subject: Infectious Diseases
Received: 2023/06/12 | Accepted: 2023/08/12 | Published: 2023/09/17
References
1. Chang L, Yan Y, Wang L. Coronavirus disease 2019: coronaviruses and blood safety. Transfus Med Rev. 2020;34(2):75-80. [DOI:10.1016/j.tmrv.2020.02.003]
2. Guan W, Ni Z, Hu Y, Liang W, Ou C, He J, et al. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med. 2020;382(18):1708-20. [DOI:10.1056/NEJMoa2002032]
3. Here's what we know about the new Omicron variant XBB.1.5 | Gavi, the Vaccine Alliance [Internet]. [cited 2023 Jan 14]. Available from: https://www.gavi.org/vaccineswork/heres-what-we-know-about-new-omicron-variant-xbb15
4. Govindan K, Mina H, Alavi B. A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19). Transp Res Part E Logist Transp Rev. 2020;138:101967. [DOI:10.1016/j.tre.2020.101967]
5. Liu Y, Wang Z, Ren J, Tian Y, Zhou M, Zhou T, et al. A COVID-19 risk assessment decision support system for general practitioners: design and development study. J Med Internet Res. 2020;22(6):e19786. [DOI:10.2196/19786]
6. Ghaderzadeh M, Asadi F. Deep Learning in the Detection and Diagnosis of COVID-19 Using Radiology Modalities: A Systematic Review. Maietta S, editor. J Healthc Eng [Internet]. 2021;2021:6677314. Available from: [DOI:10.1155/2021/6677314]
7. Abdulkareem KH, Mohammed MA, Salim A, Arif M, Geman O, Gupta D, et al. Realizing an Effective COVID-19 Diagnosis System Based on Machine Learning and IoT in Smart Hospital Environment. IEEE Internet Things J [Internet]. 2021;8(21):15919-28. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099572141&doi=10.1109%2FJIOT.2021.3050775&partnerID=40&md5=da9f1a8be5020223ff815379e01f73e2
8. Akram T, Attique M, Gul S, Shahzad A, Altaf M, Naqvi S, et al. A novel framework for rapid diagnosis of COVID-19 on computed tomography scans. Pattern Anal Appl. 2021;24(3):951-64. https://doi.org/10.1007/s10044-021-00969-x [DOI:10.1007/s10044-020-00950-0]
9. Banerjee A, Ray S, Vorselaars B, Kitson J, Mamalakis M, Weeks S, et al. Use of machine learning and artificial intelligence to predict SARS-CoV-2 infection from full blood counts in a population. Int Immunopharmacol. 2020;86:106705. [DOI:10.1016/j.intimp.2020.106705]
10. Ghaderzadeh M, Aria M, Asadi F. X-Ray Equipped with Artificial Intelligence: Changing the COVID-19 Diagnostic Paradigm during the Pandemic. Fancellu A, editor. Biomed Res Int [Internet]. 2021;2021:9942873. Available from: [DOI:10.1155/2021/9942873]
11. Ghaderzadeh M, Asadi F, Jafari R, Bashash D, Abolghasemi H, Aria M. Deep Convolutional Neural Network-Based Computer-Aided Detection System for COVID-19 Using Multiple Lung Scans: Design and Implementation Study. J Med Internet Res. 2021;23(4):e27468. [DOI:10.2196/27468]
12. Pan F, Ye T, Sun P, Gui S, Liang B, Li L, et al. Time course of lung changes on chest CT during recovery from 2019 novel coronavirus (COVID-19) pneumonia. Radiology. 2020; [DOI:10.1148/radiol.2020200370]
13. Pan Y, Guan H, Zhou S, Wang Y, Li Q, Zhu T, et al. Initial CT findings and temporal changes in patients with the novel coronavirus pneumonia (2019-nCoV): a study of 63 patients in Wuhan, China. Eur Radiol. 2020;30(6):3306-9. [DOI:10.1007/s00330-020-06731-x]
14. Ghaderzadeh M, Eshraghi MA, Asadi F, Hosseini A, Jafari R, Bashash D, et al. Efficient Framework for Detection of COVID-19 Omicron and Delta Variants Based on Two Intelligent Phases of CNN Models. Corsi C, editor. Comput Math Methods Med [Internet]. 2022;2022:4838009. Available from: [DOI:10.1155/2022/4838009]
15. Rahim A, Maqbool A, Mirza A, Afzal F, Asghar I. DepTSol: An Improved Deep-Learning- and Time-of-Flight-Based Real-Time Social Distance Monitoring Approach under Various Low-Light Conditions. Electron [Internet]. 2022;11(3). Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124364980&doi=10.3390%2Felectronics11030458&partnerID=40&md5=0128860674717312f1d9f4d81dc21f9b
16. Eshraghi MA, Ayatollahi A, Shokouhi SB. COV-MobNets: a mobile networks ensemble model for diagnosis of COVID-19 based on chest X-ray images. BMC Med Imaging [Internet]. 2023;23(1):83. Available from: [DOI:10.1186/s12880-023-01039-w]
17. Arksey H, O'Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8(1):19-32. [DOI:10.1080/1364557032000119616]
18. Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018;169(7):467-73. [DOI:10.7326/M18-0850]
19. Assaf D, Gutman Y, Neuman Y, Segal G, Amit S, Gefen-Halevi S, et al. Utilization of machine-learning models to accurately predict the risk for critical COVID-19. Intern Emerg Med. 2020;15(8):1435-43. [DOI:10.1007/s11739-020-02475-0]
20. Carvalho ARS, Guimarães A, Werberich GM, de Castro SN, Pinto JSF, Schmitt WR, et al. COVID-19 chest computed tomography to stratify severity and disease extension by artificial neural network computer-aided diagnosis. Front Med. 2020;7:577609. [DOI:10.3389/fmed.2020.577609]
21. Gull H, Krishna G, Aldossary MI, Iqbal SZ. Severity prediction of COVID-19 patients using machine learning classification algorithms: A case study of small city in Pakistan with minimal health facility. In: 2020 IEEE 6th international conference on computer and communications (ICCC). IEEE; 2020. p. 1537-41. [DOI:10.1109/ICCC51575.2020.9344984]
22. Chen X, Liu Z. Early prediction of mortality risk among severe COVID-19 patients using machine learning. 2020;
23. Wu G, Yang P, Xie Y, Woodruff HC, Rao X, Guiot J, et al. Development of a clinical decision support system for severity risk prediction and triage of COVID-19 patients at hospital admission: An international multicentre study. Eur Respir J [Internet]. 2020;56(2). Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089787826&doi=10.1183%2F13993003.01104-2020&partnerID=40&md5=8a919fecbe59c412edd2c11dc2c4bb41
24. Yan L, Zhang H-T, Xiao Y, Wang M, Sun C, Liang J, et al. 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. 2020;27:2020.
25. Sun L, Song F, Shi N, Liu F, Li S, Li P, et al. Combination of four clinical indicators predicts the severe/critical symptom of patients infected COVID-19. J Clin Virol [Internet]. 2020;128:104431. Available from: https://www.sciencedirect.com/science/article/pii/S1386653220301736 [DOI:10.1016/j.jcv.2020.104431]
26. Aswathy AL, Hareendran A, SS VC. COVID-19 diagnosis and severity detection from CT-images using transfer learning and back propagation neural network. J Infect Public Health. 2021;14(10):1435-45. [DOI:10.1016/j.jiph.2021.07.015]
27. Ahmed F, Hossain MS, Islam RU, Andersson K. An evolutionary belief rule-based clinical decision support system to predict covid-19 severity under uncertainty. Appl Sci. 2021;11(13):5810. [DOI:10.3390/app11135810]
28. Dastider AG, Sadik F, Fattah SA. An integrated autoencoder-based hybrid CNN-LSTM model for COVID-19 severity prediction from lung ultrasound. Comput Biol Med. 2021;132:104296. [DOI:10.1016/j.compbiomed.2021.104296]
29. Domínguez-Olmedo JL, Gragera-Martínez Á, Mata J, Álvarez VP. Machine learning applied to clinical laboratory data in Spain for COVID-19 outcome prediction: model development and validation. J Med Internet Res. 2021;23(4):e26211. [DOI:10.2196/26211]
30. Guan X, Zhang B, Fu M, Li M, Yuan X, Zhu Y, et al. Clinical and inflammatory features based machine learning model for fatal risk prediction of hospitalized COVID-19 patients: results from a retrospective cohort study. Ann Med. 2021;53(1):257-66. [DOI:10.1080/07853890.2020.1868564]
31. Ho TT, Park J, Kim T, Park B, Lee J, Kim JY, et al. Deep learning models for predicting severe progression in COVID-19-infected patients: Retrospective study. JMIR Med informatics. 2021;9(1):e24973. [DOI:10.2196/24973]
32. Ibrahim MR, Youssef SM, Fathalla KM. Abnormality detection and intelligent severity assessment of human chest computed tomography scans using deep learning: a case study on SARS-COV-2 assessment. J Ambient Intell Humaniz Comput. 2021;1-24. [DOI:10.1007/s12652-021-03282-x]
33. Jayaraj T, Samath JA. Disease forecasting and severity prediction model for COVID-19 using correlated feature extraction and feed-forward artificial neural networks. Int J Eng Trends Technol [Internet]. 2021;69(8):126-37. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112439628&doi=10.14445%2F22315381%2FIJETT-V69I8P216&partnerID=40&md5=08f42691f547d60c529ad4cc8735c032
34. Kang J, Chen T, Luo H, Luo Y, Du G, Jiming-Yang M. Machine learning predictive model for severe COVID-19. Infect Genet Evol. 2021;90:104737. [DOI:10.1016/j.meegid.2021.104737]
35. Kivrak M, Guldogan E, Colak C. Prediction of death status on the course of treatment in SARS-COV-2 patients with deep learning and machine learning methods. Comput Methods Programs Biomed [Internet]. 2021;201:105951. Available from: https://www.sciencedirect.com/science/article/pii/S0169260721000250 [DOI:10.1016/j.cmpb.2021.105951]
36. La Salvia M, Secco G, Torti E, Florimbi G, Guido L, Lago P, et al. Deep learning and lung ultrasound for Covid-19 pneumonia detection and severity classification. Comput Biol Med. 2021;136:104742. [DOI:10.1016/j.compbiomed.2021.104742]
37. Li Z, Zhao W, Shi F, Qi L, Xie X, Wei Y, et al. A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning. Med Image Anal. 2021;69:101978. [DOI:10.1016/j.media.2021.101978]
38. Aktar S, Ahamad MM, Rashed-Al-Mahfuz M, Azad AKM, Uddin S, Kamal AHM, et al. Machine learning approach to predicting COVID-19 disease severity based on clinical blood test data: statistical analysis and model development. JMIR Med informatics. 2021;9(4):e25884. [DOI:10.2196/25884]
39. Qiblawey Y, Tahir A, Chowdhury MEH, Khandakar A, Kiranyaz S, Rahman T, et al. Detection and severity classification of COVID-19 in CT images using deep learning. Diagnostics. 2021;11(5):893. [DOI:10.3390/diagnostics11050893]
40. Quiroz JC, Feng Y-Z, Cheng Z-Y, Rezazadegan D, Chen P-K, Lin Q-T, et al. Development and validation of a machine learning approach for automated severity assessment of COVID-19 based on clinical and imaging data: retrospective study. JMIR Med Informatics. 2021;9(2):e24572. [DOI:10.2196/24572]
41. Sayed SA-F, Elkorany AM, Mohammad SS. Applying different machine learning techniques for prediction of COVID-19 severity. Ieee Access. 2021;9:135697-707. [DOI:10.1109/ACCESS.2021.3116067]
42. Tang Z, Zhao W, Xie X, Zhong Z, Shi F, Ma T, et al. Severity assessment of COVID-19 using CT image features and laboratory indices. Phys Med Biol. 2021;66(3):35015. [DOI:10.1088/1361-6560/abbf9e]
43. Wong KC-Y, Xiang Y, Yin L, So H-C. Uncovering Clinical Risk Factors and Predicting Severe COVID-19 Cases Using UK Biobank Data: Machine Learning Approach. JMIR public Heal Surveill. 2021;7(9):e29544. [DOI:10.2196/29544]
44. Aljameel SS, Khan IU, Aslam N, Aljabri M, Alsulmi ES. Machine Learning-Based Model to Predict the Disease Severity and Outcome in COVID-19 Patients. Sci Program [Internet]. 2021;2021. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105331575&doi=10.1155%2F2021%2F5587188&partnerID=40&md5=ae8e1be4c2eb66f008c5d3704c7800a0
45. Alotaibi A, Shiblee M, Alshahrani A. Prediction of severity of covid-19-infected patients using machine learning techniques. Computers [Internet]. 2021;10(3). Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103054944&doi=10.3390%2Fcomputers10030031&partnerID=40&md5=7e3958031aaf07555070591dd8206bb2
46. Amini N, Shalbaf A. Automatic classification of severity of COVID‐19 patients using texture feature and random forest based on computed tomography images. Int J Imaging Syst Technol. 2022;32(1):102-10. [DOI:10.1002/ima.22679]
47. Blagojević A, Šušteršič T, Lorencin I, Šegota SB, Anđelić N, Milovanović D, et al. Artificial intelligence approach towards assessment of condition of COVID-19 patients - Identification of predictive biomarkers associated with severity of clinical condition and disease progression. Comput Biol Med [Internet]. 2021;138. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115011860&doi=10.1016%2Fj.compbiomed.2021.104869&partnerID=40&md5=792238ba166be6e192e1631b15ad0272
48. Chen Y, Ouyang L, Bao FS, Li Q, Han L, Zhang H, et al. A multimodality machine learning approach to differentiate severe and nonsevere COVID-19: model development and validation. J Med Internet Res. 2021;23(4):e23948. [DOI:10.2196/23948]
49. Chung H, Ko H, Kang WS, Kim KW, Lee H, Park C, et al. Prediction and feature importance analysis for severity of COVID-19 in South Korea using artificial intelligence: model development and validation. J Med Internet Res. 2021;23(4):e27060. [DOI:10.2196/27060]
50. de Fátima Cobre A, Stremel DP, Noleto GR, Fachi MM, Surek M, Wiens A, et al. Diagnosis and prediction of COVID-19 severity: can biochemical tests and machine learning be used as prognostic indicators? Comput Biol Med. 2021;134:104531. [DOI:10.1016/j.compbiomed.2021.104531]
51. Blagojević A, Šušteršič T, Lorencin I, Šegota SB, Anđelić N, Milovanović D, et al. Artificial intelligence approach towards assessment of condition of COVID-19 patients-Identification of predictive biomarkers associated with severity of clinical condition and disease progression. Comput Biol Med. 2021 Nov;138:104869. [DOI:10.1016/j.compbiomed.2021.104869]
52. Garavand A, Behmanesh A, Aslani N, Sadeghsalehi H, Ghaderzadeh M. Towards Diagnostic Aided Systems in Coronary Artery Disease Detection: A Comprehensive Multiview Survey of the State of the Art. El Kafhali S, editor. Int J Intell Syst [Internet]. 2023;2023:6442756. Available from: [DOI:10.1155/2023/6442756]
53. Amato F, López A, Peña-Méndez EM, Vaňhara P, Hampl A, Havel J. Artificial neural networks in medical diagnosis. Vol. 11, Journal of applied biomedicine. Elsevier; 2013. p. 47-58. [DOI:10.2478/v10136-012-0031-x]
54. Hu C, Liu Z, Jiang Y, Shi O, Zhang X, Xu K, et al. Early prediction of mortality risk among patients with severe COVID-19, using machine learning. Int J Epidemiol. 2020;49(6):1918-29. [DOI:10.1093/ije/dyaa171]
55. Torrealba-Rodriguez O, Conde-Gutiérrez RA, Hernández-Javier AL. Modeling and prediction of COVID-19 in Mexico applying mathematical and computational models. Chaos, Solitons & Fractals. 2020;138:109946. [DOI:10.1016/j.chaos.2020.109946]
56. Haritha D, Swaroop N, Mounika M. Prediction of COVID-19 Cases Using CNN with X-rays. In: 2020 5th International Conference on Computing, Communication and Security (ICCCS). IEEE; 2020. p. 1-6. [DOI:10.1109/ICCCS49678.2020.9276753]
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