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相关概念视频

Ethical Standards I01:25

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The American Nurses Association (ANA) created and implemented the first nationally accepted Code of Ethics for Nurses with Interpretive Statements. The Code of Ethics is a living document regularly updated by the ANA and establishes an ethical standard that is non-negotiable for nurses in all roles and settings.
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Ethical Standards II01:23

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Ethical standards are the backbone of nursing practice, guiding nurses as they interact with patients, families, and colleagues. These standards are crucial for providing safe, empathetic care centered on the patient's needs.
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An integrated healthcare system (IHS) is a set of organizations that provides for or arranges to provide coordinated and continuous service to a defined population. The IHS takes responsibility for that particular population's health status and outcome, both clinically and fiscally. An integrated healthcare system is a well-organized, well-coordinated, and collaborative network. The integrated delivery system is a network that connects different healthcare providers to deliver organized,...
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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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可解释的联合学习方案,用于安全的医疗数据共享.

Liutao Zhao1, Haoran Xie2, Lin Zhong1

  • 1Beijing Academy of Science and Technology, Beijing Computing Center Company Ltd., Beijing, China.

Health information science and systems
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这项研究引入了智能医疗保健的新型联合学习方案,提高了AI模型的解释性和数据安全性. 这种方法确保了隐私和准确的医疗数据从身体区域网络的聚合.

关键词:
可以解释的可解释性.联合学习是联合学习.医疗保健 医疗保健 医疗保健 医疗保健安全的安全的安全的安全的安全.

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科学领域:

  • 人工智能的人工智能
  • 聪明的医疗保健 智能医疗保健
  • 数据安全 数据安全

背景情况:

  • 庞大的医疗数据由可穿戴/可植入设备在身体区域网络中生成.
  • 利用人工智能来利用这些数据对于推进智能医疗应用至关重要.
  • 现有的方法在确保AI在医疗保健中的可解释性和安全性方面面临挑战.

研究的目的:

  • 为智能医疗提供创新的联合学习 (FL) 计划.
  • 解决人工智能驱动的医疗保健中可解释性和安全性的关键挑战.
  • 为了使分散的医疗数据从身体区域网络的利用.

主要方法:

  • 实施了一个FL方案,具有独立的联合建模和可解释性分析.
  • 使用后期解释技术进行全球模型分析.
  • 引入了客户私人梯度评估,以评估公平贡献.
  • 提出了一个多服务器模型,具有同型的秘密共享和散列,以确保安全聚合.

主要成果:

  • 实现了与集中培训相比的可解释性,而不会导致绩效下降.
  • 通过梯度贡献评估证明了低质量的数据的有效过.
  • 确保了强大的数据隐私和对恶意服务器的聚合正确性.
  • 在实验结果中展示了竞争力的安全性和效率.

结论:

  • 拟议的FL计划有效地平衡了智能医疗保健中的可解释性和安全性.
  • 它提供了一个强大的解决方案,用于分析来自无线身体区域网络的敏感医疗数据.
  • 这种方法释放了AI在智能医疗保健中的潜力,同时保护了患者的隐私.