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区块链赋能的医疗保健系统:通过混合深度学习提高可扩展性和安全性。

Blockchain-Powered Healthcare Systems: Enhancing Scalability and Security with Hybrid Deep Learning.

机构信息

School of IT, UNITAR International University, Petaling Jaya 47301, Malaysia.

Department of Computer System, Abdul Wali Khan University Mardan (AWKUM), Mardan 23200, Pakistan.

出版信息

Sensors (Basel). 2023 Sep 7;23(18):7740. doi: 10.3390/s23187740.

Abstract

The rapid advancements in technology have paved the way for innovative solutions in the healthcare domain, aiming to improve scalability and security while enhancing patient care. This abstract introduces a cutting-edge approach, leveraging blockchain technology and hybrid deep learning techniques to revolutionize healthcare systems. Blockchain technology provides a decentralized and transparent framework, enabling secure data storage, sharing, and access control. By integrating blockchain into healthcare systems, data integrity, privacy, and interoperability can be ensured while eliminating the reliance on centralized authorities. In conjunction with blockchain, hybrid deep learning techniques offer powerful capabilities for data analysis and decision making in healthcare. Combining the strengths of deep learning algorithms with traditional machine learning approaches, hybrid deep learning enables accurate and efficient processing of complex healthcare data, including medical records, images, and sensor data. This research proposes a permissions-based blockchain framework for scalable and secure healthcare systems, integrating hybrid deep learning models. The framework ensures that only authorized entities can access and modify sensitive health information, preserving patient privacy while facilitating seamless data sharing and collaboration among healthcare providers. Additionally, the hybrid deep learning models enable real-time analysis of large-scale healthcare data, facilitating timely diagnosis, treatment recommendations, and disease prediction. The integration of blockchain and hybrid deep learning presents numerous benefits, including enhanced scalability, improved security, interoperability, and informed decision making in healthcare systems. However, challenges such as computational complexity, regulatory compliance, and ethical considerations need to be addressed for successful implementation. By harnessing the potential of blockchain and hybrid deep learning, healthcare systems can overcome traditional limitations, promoting efficient and secure data management, personalized patient care, and advancements in medical research. The proposed framework lays the foundation for a future healthcare ecosystem that prioritizes scalability, security, and improved patient outcomes.

摘要

技术的快速进步为医疗保健领域的创新解决方案铺平了道路,旨在提高可扩展性和安全性,同时增强患者护理。本摘要介绍了一种前沿方法,利用区块链技术和混合深度学习技术来彻底改变医疗保健系统。区块链技术提供了一个去中心化和透明的框架,实现了安全的数据存储、共享和访问控制。通过将区块链集成到医疗保健系统中,可以确保数据的完整性、隐私性和互操作性,同时消除对中心化机构的依赖。结合区块链,混合深度学习技术为医疗保健中的数据分析和决策提供了强大的功能。将深度学习算法的优势与传统机器学习方法相结合,混合深度学习实现了对复杂医疗保健数据(包括病历、图像和传感器数据)的准确和高效处理。本研究提出了一种基于权限的区块链框架,用于可扩展和安全的医疗保健系统,集成了混合深度学习模型。该框架确保只有授权实体可以访问和修改敏感健康信息,保护患者隐私,同时促进医疗保健提供者之间无缝的数据共享和协作。此外,混合深度学习模型还可以实时分析大规模医疗保健数据,促进及时诊断、治疗建议和疾病预测。区块链和混合深度学习的集成带来了许多好处,包括增强的可扩展性、改进的安全性、互操作性和医疗保健系统中的明智决策。然而,仍需要解决计算复杂性、法规遵从性和道德考虑等挑战,以实现成功实施。通过利用区块链和混合深度学习的潜力,医疗保健系统可以克服传统限制,促进高效和安全的数据管理、个性化患者护理以及医学研究的进步。所提出的框架为优先考虑可扩展性、安全性和改善患者结果的未来医疗保健生态系统奠定了基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f0b2/10537957/74062a43db72/sensors-23-07740-g001.jpg

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