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Ethical Standards I01:25

Ethical Standards I

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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.
The Code of Ethics provisions outline the nurse's duty to the patient, the healthcare team, the profession, and society. The Code's fundamental principles include advocacy,...
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Issues And Trends In Healthcare Delivery System01:29

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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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Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
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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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Guidelines and Strategies for Safe Computer Charting01:18

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Health Information Technology and Healthcare Information System01:30

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Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
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医疗系统机器学习中的安全与隐私:策略和挑战

Erikson J de Aguiar1, Caetano Traina1, Agma J M Traina1

  • 1Institute of Mathematics and Computer Science, University of São Paulo, Brazil.

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概括
此摘要是机器生成的。

本研究探讨了医疗系统的机器学习 (ML) 中的安全性和隐私性,确定了关键的攻击,防御和隐私策略,如联合学习 (FL). 它强调了挑战,并为未来在这一关键领域的研究提供了指导.

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科学领域:

  • 医疗信息学 医疗信息学
  • 医疗保健中的人工智能
  • 医学领域的网络安全

背景情况:

  • 机器学习 (ML) 为医生在医疗保健中的决策支持提供了重大潜力.
  • 然而,医疗系统中的ML应用容易受到安全攻击和隐私侵犯.
  • 解决这些漏洞对于在医学中安全有效地部署ML至关重要.

研究的目的:

  • 调查和分析现有的关于医疗系统机器学习中的安全和隐私的研究.
  • 识别普遍的攻击载体,防御机制和保护隐私的策略.
  • 讨论与实施这些战略相关的挑战,并指导未来的研究.

主要方法:

  • 进行了系统的文献审查,涉及手动搜索和定义的搜索字符串.
  • 论文根据标题,摘要和全文进行过,然后对他们的贡献进行分析.
  • 收集和讨论了40篇关于健康ML中的攻击,防御和隐私的相关论文.

主要成果:

  • 确定了攻击的趋势,包括通用对抗性扰动 (UAP),基于生成对抗网络 (GAN) 的攻击和DeepFakes.
  • 突出了防御趋势,如对抗性训练,基于GAN的策略,以及对抗性示例 (AE) 的分布外 (OOD) 检测.
  • 发现了关键的隐私保护策略,包括联合学习 (FL),差异隐私和混合方法来增强FL.

结论:

  • 由于越来越多的风险和脆弱性,ML对健康的安全和隐私至关重要.
  • 该研究提供了当前战略和挑战的全面概述.
  • 这项研究旨在指导未来的研究,以确保医疗保健系统中的ML应用.