减少ZKML医疗保健应用程序的模型复杂性:保护ZKML应用程序的隐私和推理优化-使用合成ICHOM数据集的参考实现
Sathya Krishnasamy1, Ilangovan Govindarajan2
1President and Principal, ChainAim, Newington, Connecticut, USA.
网络3.0,利用去中心化网络和人工智能,面临着采用障碍. 本文探讨零知识机器学习 (ZKML) 作为全球医疗数据收集中隐私和效率的解决方案.
科学领域:
- 分权化系统和人工智能
- 在医疗保健中的加密应用.
背景情况:
- 包括分散网络和人工智能在内的Web 3.0技术正在进步,但面临着重大采用挑战.
- 之前的工作确定了在医疗保健中采用Web 3.0的障碍和缓解,重点是隐私和设计优化.
研究的目的:
- 在全球医疗保健中概念化零知识机器学习 (ZKML) 的技术和操作可行性.
- 实施使用ZKML进行大量数据收集和患者报告结果的参考医疗保健应用程序.
- 在分散的医疗保健架构中推进机器学习模型的使用,以提高数据保护和效率.
主要方法:
- 概念化ZKML的技术和运营可行性.
- 采用国际健康成果测量联盟 (ICHOM) 综合数据实施参考医疗保健系统.
- 对ICHOM糖尿病数据集的模型复杂性降低的研究.
主要成果:
- 在全球医疗保健环境中证明了ZKML的概念可行性.
- 开发了用于大量数据收集的参考实施方案,包括患者报告的结果.
- 报告了ICHOM糖尿病数据集的模型复杂性降低,提高了ML模型的适用性.
结论:
- 在Web 3.0医疗保健应用中,ZKML提供了一种可行的方法来解决隐私和推断成本的挑战.
- 该研究为在全球医疗保健标准中应用ZKML提供了基础,提高了数据保护和效率.
- 需要进一步开发,以建立ZKML在全球广泛采用医疗保健中的基线.
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