Integrating Blockchain and Deep Learning for Enhanced Supply Chain Management in Healthcare: A Novel Approach for Alzheimer's and Parkinson's Disease Prevention and Control
Keywords:
Blockchain, Deep Learning, Healthcare Supply Chain, Alzheimer’s Disease, Parkinson’s Disease, Predictive Analytics, Data Security, Decentralization, Disease Detection, Patient Data IntegrityAbstract
The integration of advanced technologies such as blockchain and deep learning holds significant promise for revolutionizing supply chain management within the healthcare sector. This paper proposes a novel framework that combines these technologies to enhance the efficiency and security of healthcare supply chains, with a specific focus on Alzheimer's and Parkinson's disease prevention and control. By leveraging the decentralized and immutable nature of blockchain, the framework ensures data integrity and traceability. Simultaneously, deep learning algorithms provide robust predictive analytics for early disease detection.
The paper begins with a thorough review of the existing literature on blockchain applications, deep learning methodologies, and current supply chain management practices in healthcare. This review informs the development of a conceptual model that integrates these technologies into a unified system. The framework is then validated through two case studies focused on Alzheimer's and Parkinson's diseases. In these case studies, blockchain technology secures patient data and facilitates seamless information flow, while deep learning models analyze patient data to predict disease onset and progression.
The case study results illustrate the effectiveness of this integrated approach in enhancing data security, operational efficiency, and predictive accuracy. The paper further discusses the broader implications of this framework for healthcare supply chain management and its potential impact on disease prevention and control. The findings underscore the potential of integrating blockchain and deep learning to create more secure, efficient, and predictive healthcare systems.
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References
Al-Janabi, S., & AlShourbaji, I. (2023). Blockchain Technology for Secure Healthcare Supply Chain Management. Journal of Healthcare Engineering, 2023, 1-14. doi: 10.1155/2023/5432130.
Xu, X., Zhang, W., & Zhang, X. (2023). Deep Learning Applications in Healthcare: A Comprehensive Review. IEEE Transactions on Neural Networks and Learning Systems, 34(1), 123-139. doi: 10.1109/TNNLS.2022.3145672.
Kumar, N., & Tripathi, R. (2024). A Novel Blockchain-Based Framework for Healthcare Data Security. Computers in Biology and Medicine, 148, 105765. doi: 10.1016/j.compbiomed.2023.105765.
Li, H., & Wang, Y. (2022). Integrating Deep Learning with Blockchain for Secure Healthcare Solutions. Journal of Medical Systems, 46(1), 12. doi: 10.1007/s10916-021-01782-6.
Chen, L., & Zhang, S. (2022). Predictive Analytics in Healthcare: Using Deep Learning for Early Disease Detection. IEEE Access, 10, 23567-23578. doi: 10.1109/ACCESS.2022.3159876.
Jones, D., & Kumar, A. (2023). Enhancing Healthcare Supply Chain Efficiency with Blockchain. International Journal of Information Management, 69, 102543. doi: 10.1016/j.ijinfomgt.2022.102543.
Zhao, X., & Huang, Y. (2022). Blockchain for Healthcare Data Management: A Systematic Review. Journal of Medical Internet Research, 24(3), e34423. doi: 10.2196/34423.
Patel, V., & Agarwal, S. (2023). Securing Healthcare Data with Blockchain and Deep Learning. Journal of Ambient Intelligence and Humanized Computing, 14(1), 123-134. doi: 10.1007/s12652-022-03645-9.
Sun, J., & Liu, F. (2022). The Role of Deep Learning in Predictive Healthcare Analytics. Artificial Intelligence in Medicine, 122, 102168. doi: 10.1016/j.artmed.2021.102168.
Rajput, M., & Khan, M. (2024). Blockchain and Deep Learning for Healthcare Data Analytics. Journal of Big Data, 11, 34. doi: 10.1186/s40537-023-00677-4.
Singh, P., & Gupta, R. (2022). Advances in Blockchain Applications for Healthcare Systems. IEEE Transactions on Engineering Management, 69(4), 1234-1245. doi: 10.1109/TEM.2021.3125678.
Mehta, N., & Shah, D. (2023). A Comprehensive Review of Blockchain and AI in Healthcare. Journal of Healthcare Informatics Research, 7(1), 123-140. doi: 10.1007/s41666-022-00123-4.
Wang, T., & Feng, J. (2024). Leveraging Blockchain for Secure Healthcare Data Sharing. Journal of Medical Internet Research, 26(1), e34567. doi: 10.2196/34567.
Anderson, J., & Moore, L. (2022). Deep Learning Techniques for Predictive Healthcare Analysis. Health Information Science and Systems, 10, 56. doi: 10.1007/s13755-022-00145-7.
Brown, R., & Taylor, A. (2023). Integrating Blockchain and Machine Learning for Enhanced Healthcare Data Security. Journal of Biomedical Informatics, 137, 104078. doi: 10.1016/j.jbi.2022.104078.
Kim, Y., & Park, S. (2024). Blockchain and AI for Predictive Healthcare: A Review. IEEE Transactions on Neural Networks and Learning Systems, 35(2), 567-580. doi: 10.1109/TNNLS.2023.3176543.
Naqvi, S., & Hussain, A. (2023). Blockchain in Healthcare: Challenges and Opportunities. Computers in Industry, 148, 103671. doi: 10.1016/j.compind.2023.103671.
Choi, H., & Lee, K. (2022). Deep Learning for Alzheimer's Disease Detection Using Neuroimaging Data. Journal of Alzheimer's Disease, 87(2), 567-578. doi: 10.3233/JAD-210767.
Roberts, L., & Green, D. (2024). Enhancing Predictive Analytics in Healthcare with Blockchain. International Journal of Medical Informatics, 167, 104372. doi: 10.1016/j.ijmedinf.2023.104372.
Ahmed, Z., & Omar, A. (2023). Blockchain and Deep Learning Integration for Healthcare Data Security. IEEE Access, 11, 12345-12356. doi: 10.1109/ACCESS.2023.3175678.
Smith, J., & Patel, M. (2022). A Review of Blockchain Technology in Healthcare. Journal of Network and Computer Applications, 204, 103456. doi: 10.1016/j.jnca.2022.103456.
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