基于电子医疗保健的游牧式多代理隐私指标:深度学习方法
Chandramohan Dhasarathan1, M Shanmugam2, Manish Kumar1
1Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab India.
这项研究引入了一种新的多代理系统,使用隐私指标来确保电子医疗中的深度学习. 它增强了数据保护和诊断准确性,以获得更好的患者结果.
科学领域:
- 人工智能的人工智能
- 医疗保健信息学 医疗保健信息学
- 网络安全 网络安全
背景情况:
- 电子医疗的深度学习提供了诊断和治疗的好处,但引入了重要的患者数据隐私风险.
- 确保患者数据安全在电子医疗应用中至关重要.
研究的目的:
- 提出一种新的方法,将深度学习和多代理系统结合起来,以提高电子医疗保健中的隐私.
- 实施基于多代理的隐私指标,以实时监控和控制患者数据访问.
主要方法:
- 开发了一个多代理系统来管理数据访问,将角色和权限分配给代理.
- 隐私指标 (机密性,完整性,可用性) 用于实时评估数据访问.
- 集成了一个深度学习模型,以提高诊断和治疗计划的准确性.
主要成果:
- 该系统确保只有授权代理才能访问患者数据,大大提高了电子医疗保健的安全性.
- 实时隐私指标评估有效地识别了潜在的隐私侵犯,降低了违规风险.
- 综合深度学习模型在诊断和治疗规划方面表现出更高的准确性.
结论:
- 基于多个代理的隐私指标方法为安全的电子医疗深度学习提供了强大的框架.
- 这个集成系统平衡了先进的人工智能功能与必要的患者数据隐私.
- 这种方法可以通过提高安全性和诊断精度来改善患者的治疗结果.
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