在医疗保健5.0中推进流行病防范:联邦学习应用调查

IF 2.3 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Saeed Hamood Alsamhi, Ammar Hawbani, Alexey V. Shvetsov, Santosh Kumar
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引用次数: 0

摘要

联邦学习(FL)和医疗保健5.0的结合有望带来一个更具弹性的未来,特别是在大流行防范方面。在此上下文中,Healthcare 5.0意味着医疗保健交付的整体方法,其中相互关联的技术支持数据驱动的决策、以患者为中心的护理和提高效率。本文深入探讨了FL在Healthcare 5.0框架内的作用及其对大流行应对的影响。具体而言,FL在几个重要方面为医疗保健5.0中的大流行防范提供了革命性的潜力:它支持在不损害个人数据隐私的情况下从分布式数据源进行协作学习,通过授权当地医疗保健机构为集体知识库做出贡献来促进分散决策,并增强实时监控,从而能够及早发现疫情并做出明智的响应。我们首先阐述FL和Healthcare 5.0的概念,然后分析当前的大流行防范和响应机制。我们深入研究了FL在医疗保健领域的应用和案例研究,强调了其潜在的好处,包括隐私保护、分散决策和实施挑战。通过阐明FL如何适应Healthcare 5.0,我们设想了未来在技术集成医疗系统中的应用。通过检查FL在医疗保健中的当前应用和案例研究,我们强调了其潜在的好处,包括增强隐私保护和更有效的决策支持系统。我们的研究结果表明,FL可以显著改善大流行的反应时间和准确性。此外,我们还推测了FL可能加强大流行防范并使医疗保健更具弹性的潜在情况。最后,我们建议政策制定者、技术专家和教育工作者应对潜在的挑战,并在Healthcare 5.0中最大化FL的好处。本文旨在为下一代医疗保健技术的论述做出贡献,强调FL在塑造更具弹性的医疗保健未来方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Advancing Pandemic Preparedness in Healthcare 5.0: A Survey of Federated Learning Applications
The intersection of Federated Learning (FL) and Healthcare 5.0 promises a transformative shift towards a more resilient future, particularly concerning pandemic preparedness. Within this context, Healthcare 5.0 signifies a holistic approach to healthcare delivery, where interconnected technologies enable data-driven decision-making, patient-centric care, and enhanced efficiency. This paper provides an in-depth exploration of FL’s role within the framework of Healthcare 5.0 and its implications for the pandemic response. Specifically, FL offers the potential to revolutionize pandemic preparedness within Healthcare 5.0 in several vital ways: it enables collaborative learning from distributed data sources without compromising individual data privacy, facilitates decentralized decision-making by empowering local healthcare institutions to contribute to a collective knowledge pool, and enhances real-time surveillance, enabling early detection of outbreaks and informed responses. We start by laying out the concepts of FL and Healthcare 5.0, followed by an analysis of current pandemic preparedness and response mechanisms. We delve into FL’s applications and case studies in healthcare, highlighting its potential benefits, including privacy protection, decentralized decision-making, and implementation challenges. By articulating how FL fits into Healthcare 5.0, we envisage future applications in a technologically integrated health system. By examining current applications and case studies of FL in healthcare, we highlight its potential benefits, including enhanced privacy protection and more effective decision support systems. Our findings demonstrate that FL can significantly improve pandemic response times and accuracy. Moreover, we speculate on the potential scenarios where FL could enhance pandemic preparedness and make healthcare more resilient. Finally, we recommend that policymakers, technologists, and educators address potential challenges and maximize the benefits of FL in Healthcare 5.0. This paper aims to contribute to the discourse on next-generation healthcare technologies, emphasizing FL’s potential to shape a more resilient healthcare future.
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来源期刊
Advances in Human-Computer Interaction
Advances in Human-Computer Interaction COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
6.30
自引率
3.40%
发文量
22
审稿时长
36 weeks
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