An Intelligent Blockchain-Based System Configuration for Screening, Monitoring, and Tracing of Pandemics | ||
| Journal of AI and Data Mining | ||
| دوره 12، شماره 2، تیر 2024، صفحه 163-191 اصل مقاله (1.37 M) | ||
| نوع مقاله: Original/Review Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2024.14379.2541 | ||
| نویسندگان | ||
| Ali Rebwar Shabrandi* 1؛ Ali Rajabzadeh Ghatari2؛ Mohammad Dehghan nayeri2؛ Nader Tavakoli3؛ Sahar Mirzaei4 | ||
| 1Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran. | ||
| 2Department of Industrial Management, Faculty of Management and Economics, Tarbiat Modares University. | ||
| 3Department of Emergency Medicine, Trauma and Injury Research Center, Iran University of Medical Sciences. | ||
| 4Iran University of Medical Sciences, Tehran, Iran. | ||
| چکیده | ||
| This study proposes a high-level design and configuration for an intelligent dual (hybrid and private) blockchain-based system. The configuration includes the type of network, level of decentralization, nodes, and roles, block structure information, authority control, and smart contracts and intended to address the two main categories of challenges–operation management and data management–through three intelligent modules across the pandemic stages. In the pre-hospital stage, an intelligent infection prediction system is proposed that utilizes in-house data to address the lack of a simple, efficient, agile, and low-cost screening method for identifying potentially infected individuals promptly and preventing the overload of patients entering hospitals. In the in-hospital stage, an intelligent prediction system is proposed to predict infection severity and hospital Length of Stay (LoS) to identify high-risk patients, prioritize them for receiving care services, and facilitate better resource allocation. In the post-hospital stage, an intelligent prediction system is proposed to predict the reinfection and readmission rates, to help reduce the burden on the healthcare system and provide personalized care and follow-up for higher-risk patients. In addition, the distribution of limited Personal protective equipment (PPE) is made fair using private blockchain (BC) and smart contracts. These modules were developed using Python and utilized to evaluate the performance of state-of-the-art machine learning (ML) techniques through 10-fold cross-validation at each stage. The most critical features were plotted and analyzed using SHapely Adaptive exPlanations (SHAP). Finally, we explored the implications of our system for both research and practice and provided recommendations for future enhancements. | ||
| کلیدواژهها | ||
| Pandemic؛ Blockchain؛ Artificial Intelligence؛ Configuration؛ System | ||
| مراجع | ||
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[1] Ammirato, S., R. Linzalone, and A.M. Felicetti, Knowledge management in pandemics. A critical literature review. Knowledge Management Research & Practice, vol.19, no. 4, pp. 415-426, 2021.
[2] Cavallo, A. and V. Ireland, Prepared for the Global Assessment Report on Disaster Risk Reduction 2015. International Journal of Disaster Risk Reduction, 2014.
[3] Adiga, A., et al., Data-Driven Modeling for Different Stages of Pandemic Response. Journal of the Indian Institute of Science, vol. 100, no. 4, pp. 901-915, 2020.
[4] WHO. WHO Director-General's opening remarks at the media briefing on COVID-19 - 11 March 2020. 2020 [cited 2023 June. 1, 2023].
[5] Zhai, Y., et al. From isolation to coordination: how can telemedicine help combat the COVID-19 outbreak. in Digital Health and Medical Analytics: Second International Conference, DHA 2020, Beijing, China, Revised Selected Papers 2. 2021. Springer, July 25, 2020.
[6] Looi, M.-K., Covid-19: Is a second wave hitting Europe? bmj, 2020. 371.
[7] Abebe, E.C., et al., The newly emerged COVID-19 disease: a systemic review. Virology journal, vol. 17, no. 1, pp. 1-8, 2020.
[8] Muhammad, G. and M.S. Hossain, COVID-19 and non-COVID-19 classification using multi-layers fusion from lung ultrasound images. Information Fusion, vol. 72, pp. 80-88, 2021.
[9] Dos Santos, L.A., et al., Recurrent COVID-19 including evidence of reinfection and enhanced severity in thirty Brazilian healthcare workers. Journal of Infection, vol. 82, no. 3, pp. 399-406, 2021.
[10] Li, Y., et al., Stability issues of RT‐PCR testing of SARS‐CoV‐2 for hospitalized patients clinically diagnosed with COVID‐19. Journal of medical virology, vol. 92, no. 7, pp. 903-908, 2020.
[11] Wang, S., et al., A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis. European Respiratory Journal, vol. 56, no. 2, 2020.
[12] Banerjee, A., et al., Use of machine learning and artificial intelligence to predict SARS-CoV-2 infection from full blood counts in a population. International immunopharmacology, vol. 86, pp. 106705, 2020.
[13] Arpaci, I., et al., Predicting the COVID-19 infection with fourteen clinical features using machine learning classification algorithms. Multimedia Tools and Applications, vol. 80, pp. 11943-11957, 2021.
[14] Grannec, F., et al., Pre-Hospital Management of Patients with COVID-19 and the Impact on Hospitalization. Medicina 2023, vol. 59, pp. 1440, 2023.
[15] Son, K.-B., T.-j. Lee, and S.-s. Hwang, Disease severity classification and COVID-19 outcomes, Republic of Korea. Bulletin of the World Health Organization, vol. 99, no. 1, pp. 62, 2021.
[16] Breathnach, A.S., et al., Prior COVID-19 significantly reduces the risk of subsequent infection, but reinfections are seen after eight months. The Journal of Infection, vol. 82, no.4, pp. 11, 2021.
[17] Gupta, V., et al., A symptomatic Reinfection in 2 Healthcare Workers from India with Genetically Distinct Severe Acute Respiratory Syndrome Coronavirus 2. Clinical Infectious Diseases, vol.73, no.9, 2020.
[18] Nagesh, S. and S. Chakraborty, Saving the frontline health workforce amidst the COVID-19 crisis: challenges and recommendations. Journal of global health, vol. 10, no.1, 2020.
[19] Zhu, P., et al., Enhancing Traceability of Infectious Diseases: A Blockchain-Based Approach. Information Processing and Management, vol. 58, no. 4, 2021.
[20] Pandey, P. and R. Litoriya, Securing and authenticating healthcare records through blockchain technology. Cryptologia, Vol. 44, no. 4, pp. 341-356, 2020.
[21] Ismail, L., H. Materwala, and S. Zeadally, Lightweight blockchain for healthcare. IEEE Access, vol. 7, pp. 149935-149951, 2019.
[22] Lau, H., et al., Internationally lost COVID-19 cases. Journal of Microbiology, Immunology and Infection, vol. 53, no. 3, pp. 454-458, 2020.
[23] Gao, F., et al., Management and Data Sharing of COVID-19 Pandemic Information. Biopreservation and Biobanking, vol. 18, no. 6, pp. 570-580, 2020.
[24] Rasheed, J., et al., COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review. Interdisciplinary Sciences: Computational Life Sciences, vol. 13, pp. 1-23, 2021.
[25] WHO. Coronavirus Disease 2019 (COVID-19): Situation report—30. 2020.
[26] Coppola, L., et al., Biobanking in health care: evolution and future directions. Journal of translational medicine, vol. 17, no.1, pp. 1-18, 2019.
[27] Yaqoob, I., et al., Blockchain for healthcare data management: opportunities, challenges, and future recommendations. Neural Computing and Applications, 2020: pp. 1-16.
[28] Spreeuwenberg, P., M. Kroneman, and J. Paget, Reassessing the global mortality burden of the 1918 influenza pandemic. American journal of epidemiology, vol. 187, no. 12, pp. 2561-2567, 2018.
[29] Al-Emran, M., Hierarchical reinforcement learning: a survey. International journal of computing and digital systems, vol. 4, no. 02, 2015.
[30] Arpaci, I., et al., Analysis of twitter data using evolutionary clustering during the COVID-19 pandemic. Computers, Materials & Continua, vol. 65, no. 1, pp. 193-204, 2020.
[31] Mhamdi, C., M. Al-Emran, and S.A. Salloum, Text mining and analytics: A case study from news channels posts on Facebook, in Intelligent Natural Language Processing: Trends and Applications. pp. 399-415. Springer, 2018.
[32] Zaza, S. and M. Al-Emran. Mining and exploration of credit cards data in UAE. in 2015, Fifth International Conference on e-Learning (econf), IEEE, 2015.
[33] Bian, J. and F. Modave, The rapid growth of intelligent systems in health and health care. 2020, SAGE Publications Sage UK: London, England. pp. 5-7.
[34] Nguyen, T. T., Nguyen, Q. V. H., Nguyen, D. T., Yang, S., Eklund, P. W., Huynh-The, T., ... & Hsu, E. B. (2020). Artificial intelligence in the battle against coronavirus (COVID-19): a survey and future research directions. arXiv preprint arXiv:2008.07343.
[35] Shabrandi, A., et al., Fast COVID-19 Infection Prediction with In-House Data Using Machine Learning Classification Algorithms: A Case Study of Iran. Journal of AI and Data Mining, pp. 573-585, 2024.
[36] Aslan, M.F., et al., CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection. Applied Soft Computing, vol. 98 pp. 106912, 2021.
[37] Wang, B., et al., AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system. Applied Soft Computing, vol. 98, pp. 106897, 2021.
[38] Chandra, T.B., et al., Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting-based classifier ensemble. Expert systems with applications, vol. 165, pp. 113909, 2021.
[39] Lassau, N., et al., Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients. Nature communications, vol. 12, no.1, pp. 1-11, 2021.
[40] Afrash, M.R., et al., Predicting hospital readmission risk in patients with COVID-19: a machine learning approach. Informatics in medicine unlocked, vol. 30, pp. 100908, 2022.
[41] Long, J.B. and J.M. Ehrenfeld, The role of augmented intelligence (AI) in detecting and preventing the spread of novel coronavirus. 2020, Springer.
[42] Gozes, O., et al., Rapid ai development cycle for the coronavirus (covid-19) pandemic: Initial results for automated detection & patient monitoring using deep learning ct image analysis. arXiv:2003.05037, 2020.
[43] Jin, C., et al., Development and evaluation of an artificial intelligence system for COVID-19 diagnosis. Nature communications, vol.11, no. 1, pp. 1-14, 2020.
[44] Punn, N.S. and S. Agarwal, Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks. Applied Intelligence, vol. 51, no. 5, pp. 2689-2702, 2021.
[45] Song, Y., et al., Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images. IEEE/ACM transactions on computational biology and bioinformatics, vol. 18, no. 6, pp. 2775-2780, 2021.
[46] Wang, S., et al., A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19). European radiology, vol. 31, pp. 1-9, 2021.
[47] Tostmann, A., et al., Strong associations and moderate predictive value of early symptoms for SARS-CoV-2 test positivity among healthcare workers, the Netherlands, March 2020. Eurosurveillance, vol. 25, no. 16, pp. 2000508, 2020.
[48] Punn, N.S., S.K. Sonbhadra, and S. Agarwal, COVID-19 epidemic analysis using machine learning and deep learning algorithms. MedRxiv, 2020.
[49] Feng, C., et al., A novel triage tool of artificial intelligence assisted diagnosis aid system for suspected COVID-19 pneumonia in fever clinics. MedRxiv, pp. 2020.03. 19.20039099, 2021.
[50] Mei, X., et al., Artificial intelligence–enabled rapid diagnosis of patients with COVID-19. Nature medicine, vol. 26, no. 8, pp. 1224-1228, 2020.
[51] Shaverdian, N., et al., Need for caution in the diagnosis of radiation pneumonitis during the covid-19 pandemic. Advances in radiation oncology, vol. 5, no. 4, pp. 617-620, 2020.
[52] Guhathakurata, S., et al., A novel approach to predict COVID-19 using support vector machine, in Data Science for COVID-19. Elsevier. pp. 351-364, 2021.
[53] Guhathakurata, S., et al., A new approach to predict COVID-19 using artificial neural networks, in Cyber-physical systems, Elsevier, pp. 139-160, 2022.
[54] Malik, M., et al., Determination of COVID-19 Patients Using Machine Learning Algorithms. Intelligent Automation & Soft Computing, vol. 31, no. 1, 2022.
[55] Buttia, C., et al., Prognostic models in COVID-19 infection that predict severity: a systematic review. European journal of epidemiology, vol. 38, no. 4, pp. 355-372, 2023.
[56] Liu, L., et al., Predicting COVID-19 Severity: Challenges in Reproducibility and Deployment of Machine Learning Methods. International Journal of Medical Informatics, pp. 105210, 2023.
[57] Mahboub, B., et al., Prediction of COVID-19 hospital length of stay and risk of death using artificial intelligence-based modeling. Frontiers in medicine, vol. 8, pp. 592336, 2021.
[58] Ebinger, J., et al., A machine learning algorithm predicts duration of hospitalization in COVID-19 patients. Intelligence-based medicine, vol. 5, pp. 100035, 2021.
[59] Alabbad, D.A., et al., Machine learning model for predicting the length of stay in the intensive care unit for COVID-19 patients in the eastern province of Saudi Arabia. Informatics in Medicine Unlocked, vol. 30, pp. 100937, 2022.
[60] Afrash, M., et al., Predictive modeling of hospital length of stay in COVID-19 patients using machine learning algorithms. Journal of Medicinal and Chemical Sciences, vol. 4, no. 5, pp. 525-537, 2021.
[61] Jutzeler, C.R., et al., Comorbidities, clinical signs and symptoms, laboratory findings, imaging features, treatment strategies, and outcomes in adult and pediatric patients with COVID-19: A systematic review and meta-analysis. Travel medicine and infectious disease, vol. 37, pp. 101825, 2020.
[62] Saadatmand, S., et al., Using machine learning in prediction of ICU admission, mortality, and length of stay in the early stage of admission of COVID-19 patients. Annals of Operations Research, vol. 328, no. 1, pp. 1043-1071, 2023.
[63] Amiri, P., et al., Prediction of mortality risk and duration of hospitalization of COVID-19 patients with chronic comorbidities based on machine learning algorithms. Digital Health, vol. 9, pp. 20552076231170493, 2023.
[64] Alafif, T., et al., Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions. International Journal of Environmental Research and Public Health, vol. 18, no. 3, pp. 1117. 2021.
[65] Nayak, J., et al., Intelligent system for COVID-19 prognosis: a state-of-the-art survey. Applied Intelligence: pp. 1-31.
[66] Syeda, H.B., et al., Role of machine learning techniques to tackle the COVID-19 crisis: Systematic review. JMIR medical informatics, vol. 9, no. 1, pp. e23811, 2021.
[67] Lalmuanawma, S., J. Hussain, and L. Chhakchhuak, Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review. Chaos, Solitons & Fractals, pp. 110059, 2020.
[68] Tayarani-N, M.-H., Applications of artificial intelligence in battling against Covid-19: a literature review. Chaos, Solitons & Fractals, pp. 110338, 2020.
[69] El-Bouzaidi, Y.E.I. and O. Abdoun, Advances in Artificial Intelligence for Accurate and Timely Diagnosis of COVID-19: A Comprehensive Review of Medical Imaging Analysis. Scientific African, pp. e01961, 2023.
[70] Burnazovic, E., et al., Application of Artificial intelligence in COVID-19-related geriatric care: A scoping review. Archives of Gerontology and Geriatrics, vol. 116, pp. 105129, 2024.
[71] Khan, M., et al., Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review. Expert Systems with Applications, vol. 185, pp. 115695, 2021.
[72] Comito, C. and C. Pizzuti, Artificial intelligence for forecasting and diagnosing COVID-19 pandemic: A focused review. Artificial Intelligence in Medicine, vol. 128: pp. 102286, 2022.
[73] Tayarani N, M.-H., Applications of artificial intelligence in battling against covid-19: A literature review. Chaos, Solitons & Fractals, vol. 142, pp. 110338, 2021.
[74] Yi, J., et al., Review on the COVID-19 pandemic prevention and control system based on AI. Engineering Applications of Artificial Intelligence, vol. 114: pp. 105184, 2022.
[75] Nakamoto, S., Bitcoin: A peer-to-peer electronic cash system. Decentralized business review, 2008.
[76] Sullivan, C. and E. Burger, E-residency and blockchain. Computer Law & Security Review, vol. 33, no. 4, pp. 470-481, 2017.
[77] Yüksel, B., A. Küpçü, and Ö. Özkasap, Research issues for privacy and security of electronic health services. Future Generation Computer Systems, vol. 68, pp. 1-13, 2017.
[78] Kshetri, N., Blockchain's roles in strengthening cybersecurity and protecting privacy. Telecommunications Policy, vol. 41, no. 10, pp. 1027-1038, 2017.
[79] Beninger, P. and M.A. Ibara, Pharmacovigilance and Biomedical Informatics: A Model for Future Development. Clinical Therapeutics, vol. 38, no. 12, pp. 2514-2525, 2016.
[80] Au, M.H., et al., A general framework for secure sharing of personal health records in cloud system. Journal of Computer and System Sciences, vol. 90, pp. 46-62, 2017.
[81] Wang, S., et al., Blockchain-powered parallel healthcare systems based on the ACP approach. IEEE Transactions on Computational Social Systems, vol. 5, no. 4, pp. 942-950, 2018.
[82] Andrew, J., et al., Blockchain for healthcare systems: Architecture, security challenges, trends and future directions. Journal of Network and Computer Applications, pp. 103633, 2023.
[83] IBM. How the FDA is piloting blockchain for the pharmaceutical supply chain. 2020 [cited 2021 3 Apr 2021]; Available from: https://www.ibm.com/blogs/blockchain/2020/05/how-the-fda-is-piloting-blockchain-for-the-pharmaceutical-supply-chain/.
[84] N., A. Blockchain in healthcare: use cases for your practice. 2020 [cited 2021 3 Apr 2021]; Available from: https://www.itransition.com/blog/blockchain-in-healthcare.
[85] Yong, B., et al., An intelligent blockchain-based system for safe vaccine supply and supervision. International Journal of Information Management, vol. 52, pp. 102024, 2020.
[86] Hussien, H.M., et al., Blockchain technology in the healthcare industry: Trends and opportunities. Journal of Industrial Information Integration, vol. 22: pp. 100217, 2021.
[87] He, W., Z.J. Zhang, and W. Li, Information technology solutions, challenges, and suggestions for tackling the COVID-19 pandemic. International Journal of Information Management, vol. 57, pp. 102287, 2021.
[88] Stafford, T.F. and H. Treiblmaier, Characteristics of a Blockchain Ecosystem for Secure and Sharable Electronic Medical Records. IEEE Transactions on Engineering Management, vol. 67, no. 4, pp. 1340-1362, 2020.
[89] Zhang, A. and X. Lin, Towards Secure and Privacy-Preserving Data Sharing in e-Health Systems via Consortium Blockchain. Journal of Medical Systems, vol. 42, no. 8, pp. 140, 2018.
[90] Islam, A., et al., A Blockchain-Based Artificial Intelligence-Empowered Contagious Pandemic Situation Supervision Scheme Using Internet of Drone Things. IEEE Wireless Communications, 28(4), pp. 166-173, 2021.
[91] Kumar, A., et al., A drone-based networked system and methods for combating coronavirus disease (COVID-19) pandemic. Future Generation Computer Systems, vol. 115, pp. 1-19, 2021.
[92] Gupta, R., et al., Blockchain-envisioned softwarized multi-swarming UAVs to tackle COVID-I9 situations. IEEE network, vol. 35, no. 2, pp. 160-167, 2020.
[93] Karaarslan, E. and D. Aydın, 2 - An artificial intelligence–based decision support and resource management system for COVID-19 pandemic, in Data Science for COVID-19, U. Kose, et al., Editors. 2021, Academic Press. pp. 25-49.
[94] Wadud, M.A.H., et al. A Patient Centric Agent Assisted Private Blockchain on Hyperledger Fabric for Managing Remote Patient Monitoring. in 2020 11th International Conference on Electrical and Computer Engineering (ICECE), 2020.
[95] Saha, R., et al., Internet of Things Framework for Oxygen Saturation Monitoring in COVID-19 Environment. IEEE Internet of Things Journal, vol. 9, no. 5l, pp. 3631-3641, 2022.
[96] Shrimali, B. and H.B. Patel, Blockchain state-of-the-art: architecture, use cases, consensus, challenges and opportunities. Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 9, pp. 6793-6807, 2022.
[97] Xie, M., et al., A survey on blockchain consensus mechanism: research overview, current advances and future directions. International Journal of Intelligent Computing and Cybernetics, vol. 16, no. 2, pp. 314-340, 2023.
[98] Shrier, D., W. Wu, and A. Pentland, Blockchain & infrastructure (identity, data security). Massachusetts Institute of Technology-Connection Science, vol. 1, no. 3, pp. 1-19, 2016.
[99] Mitani, T. and A. Otsuka, Traceability in permissioned blockchain. IEEE Access, vol. 8, pp. 21573-21588, 2020.
[100] Almeshal, T.A. and A.A. Alhogail, Blockchain for businesses: a scoping review of suitability evaluations frameworks. IEEE Access, vol. 9, pp. 155425-155442, 2021.
[101] Xu, X., et al. The blockchain as a software connector. in 2016 13th Working IEEE/IFIP Conference on Software Architecture (WICSA), IEEE, 2016,
[102] Lo, S.K., et al. Evaluating suitability of applying blockchain. in 2017, 22nd international conference on engineering of complex computer systems (ICECCS), IEEE, 2017.
[103] Schneider, B. and W. Azan. Perceptions and Misconceptions of Blockchain: The Potential of Applying Threshold Concept Theory. in 2022 IEEE 6th International Conference on Logistics Operations Management (GOL), IEEE, 2022.
[104] Betzwieser, B., Franzbonenkamp, S., Riasanow, T., Böhm, M., Kienegger, H., & Krcmar, H. (2019). A decision model for the implementation of blockchain solutions.
[105] Simaremare, A., et al. Suitability study of Blockchain application in electric utility company business processes. in IOP Conference Series: Materials Science and Engineering. IOP Publishing, 2021.
[106] Masood, F., & Faridi, A. R. A multi-criteria decision-making approach to analyse the viability of blockchain in software development projects. Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp: 113-124, 2023.
[107] Spencer-Hicken, S., C. Schutte, and P. Vlok, Blockchain feasibility assessment: A quantitative approach. Frontiers in Blockchain, vol. 6, pp. 1067039, 2023.
[108] Peck, M.E., Blockchain world-Do you need a blockchain? This chart will tell you if the technology can solve your problem. IEEE Spectrum, vol. 54, no. 10, pp. 38-60, 2017.
[109] Wüst, K. and A. Gervais. Do you need a blockchain? in 2018 crypto valley conference on blockchain technology (CVCBT). IEEE, 2018.
[110] Emmadi, N., et al. Practical deployability of permissioned blockchains. in Business Information Systems Workshops: BIS 2018 International Workshops, Berlin, Germany, July 18–20, 2018, Revised Papers 21. 2019. Springer.
[111] Hunhevicz, J.J. and D.M. Hall, Do you need a blockchain in construction? Use case categories and decision framework for DLT design options. Advanced Engineering Informatics, vol. 45, pp. 101094, 2020.
[112] Pedersen, A.B., M. Risius, and R. Beck, A ten-step decision path to determine when to use blockchain technologies. MIS Quarterly Executive, vol. 18, no. 2, pp. 99-115.
[113] Hassija, V., et al., Framework for determining the suitability of blockchain: Criteria and issues to consider. Transactions on Emerging Telecommunications Technologies, vol. 32, no. 10, pp. e4334, 2021.
[114] El Madhoun, N., J. Hatin, and E. Bertin. Going beyond the blockchain hype: In which cases are blockchains useful for its applications? in 2019 3rd Cyber Security in Networking Conference (CSNet). IEEE, 2019.
[115] Koens, T. and E. Poll. What blockchain alternative do you need? in Data Privacy Management, Cryptocurrencies and Blockchain Technology: ESORICS 2018 International Workshops, DPM 2018 and CBT 2018, Barcelona, Spain, September 6-7, 2018, Proceedings 13. 2018. Springer.
[116] Loo, W.K., et al., Systematic review on COVID-19 readmission and risk factors: future of machine learning in COVID-19 readmission studies. Frontiers in public health, vol. 10, pp. 898254l, 2022.
[117] Basile, D., et al., Digitalizing circular economy through Blockchains: The blockchain circular economy index. Journal of Industrial and Production Engineering, vol. 40, no. 4, pp. 233-245, 2023.
[118] XDC. XDC Network. 2023 [cited 2023 11/24/2023]; Available from: https://xdc.org/.
[119] Kushwaha, S.S., et al., Ethereum smart contract analysis tools: A systematic review. IEEE Access, vol. 10, pp. 57037-57062, 2022.
[120] Grishchenko, I., M. Maffei, and C. Schneidewind. A semantic framework for the security analysis of ethereum smart contracts. in International Conference on Principles of Security and Trust. 2018. Springer.
[121] Christidis, K. and M. Devetsikiotis, Blockchains and smart contracts for the internet of things. IEEE Access, 2016. vol. 4, pp. 2292-2303.
[122] Burki, T., Global shortage of personal protective equipment. The Lancet Infectious Diseases, vol. 20, no. 7, pp. 785-786, 2020.
[123] Wirth, R. and J. Hipp. CRISP-DM: Towards a standard process model for data mining. in Proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining. 2000. Manchester.
[124] Wen, Y., et al., Time-to-event modeling for hospital length of stay prediction for COVID-19 patients. Machine Learning with Applications, vol. 9, pp. 100365, 2022.
[125] Alansari, S.A., et al., Efficient and privacy-preserving infection control system for COVID-19-like pandemics using blockchain. IEEE Internet of Things Journal, vol. 9, no. 4, pp. 2744-2760, 2021.
[126] Ahmed, I., A. Chehri, and G. Jeon, Artificial Intelligence and Blockchain Enabled Smart Healthcare System for Monitoring and Detection of COVID-19 in Biomedical Images. IEEE/ACM Transactions on Computational Biology and Bioinformatics, pp. 1-10, 2023.
[127] Ahmad, R.W., et al., Blockchain and COVID-19 pandemic: Applications and challenges. Cluster Computing, pp. 1-26, 2023.
[128] Caceres, M.M.F., et al., The impact of misinformation on the COVID-19 pandemic. AIMS public health, vol. 9, no. 2, pp. 262, 2022.
[129] Ceci, Flavio Maria, Marco Fiore, Francesca Gavaruzzi, Antonio Angeloni, Marco Lucarelli, Carolina Scagnolari, Enea Bonci et al. "Early routine biomarkers of SARS-CoV-2 morbidity and mortality: outcomes from an emergency section." Di, agnostics, vol. 12, no. 1, p. 176, 2022.
[130] Piri, Seyed Mohammad, Maryam Edalatfar, Sina Shool, Mohammad Naser Jalalian, and Soheil Tavakolpour. "A systematic review on the recurrence of SARS-CoV-2 virus: frequency, risk factors, and possible explanations." Infectious Diseases, vol. 53, no. 5, pp. 315-324, 2021.
[131] Dhillon, Rubaid Azhar, Mohammad Aadil Qamar, Jaleed Ahmed Gilani, Omar Irfan, Usama Waqar, Mir Ibrahim Sajid, and Syed Faisal Mahmood. "The mystery of COVID-19 reinfections: A global systematic review and meta-analysis." Annals of Medicine and Surgery, vol. 72, pp.103130, 2021. | ||
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