论文标题
计算机上的智能医疗系统的分散机器学习
Decentralized Machine Learning for Intelligent Health Care Systems on the Computing Continuum
论文作者
论文摘要
电子个人健康记录（EHR）的引入使不同的医疗保健系统之间的全国信息交流和策展。但是，当前的EHR系统没有提供透明的诊断支持，医学研究的手段，或者可以利用个人医疗设备生成的无所不在数据。此外，EHR系统是在集中精心策划的，这可能会导致单点故障。因此，在本文中，我们探讨了将机器学习分散到分布式分类帐上的新颖方法，以创建智能的EHR系统，这些系统可以利用个人医疗设备的信息来改善知识提取。因此，我们提出并评估了一个概念EHR，以实现多个医疗机构的匿名预测分析。评估结果表明，分散的EHR可以在计算连续体中部署，而机器学习时间缩短了60％，共识延迟低于8秒。
The introduction of electronic personal health records (EHR) enables nationwide information exchange and curation among different health care systems. However, the current EHR systems do not provide transparent means for diagnosis support, medical research or can utilize the omnipresent data produced by the personal medical devices. Besides, the EHR systems are centrally orchestrated, which could potentially lead to a single point of failure. Therefore, in this article, we explore novel approaches for decentralizing machine learning over distributed ledgers to create intelligent EHR systems that can utilize information from personal medical devices for improved knowledge extraction. Consequently, we proposed and evaluated a conceptual EHR to enable anonymous predictive analysis across multiple medical institutions. The evaluation results indicate that the decentralized EHR can be deployed over the computing continuum with reduced machine learning time of up to 60% and consensus latency of below 8 seconds.
