EdgeLaaS：边缘学习即以知识为中心的互联医疗服务
EdgeLaaS: Edge Learning as a Service for Knowledge-Centric Connected Healthcare
摘要：
通过将网络技术和服务引入医疗保健基础设施（例如，随着部署多模态传感器和智能设备来监督人的健康状况，传统的医疗保健系统正在朝着以知识为中心的互联医疗保健（KCCH）方向进行革命，在这种医疗保健系统中，人们将以知识为中心的方式对自己的医疗保健负责。由于由这些医疗保健基础设施生成的医疗保健监督数据的量、速度和种类，迫切的和战略性的问题是如何利用正确监护人的正确知识（例如，医生、护士、护士在正确的时间。针对这一问题，研究了医学知识的命名和路由规范。有了这些卸载的医疗知识，我们提出了一个边缘学习即服务（EdgeLaaS）框架，用于KCCH本地处理健康监督数据。在这个框架中，边缘学习节点可以帮助患者在一些紧急情况发生时实时的从正确的监护人那里选择更好的建议。研究了两个应用案例：1）快速自助和2）移动帮助预呼叫。性能评估分别证明了KCCH和EdgeLaaS的优越性。
英文摘要：
By introducing networking technologies and services into healthcare infrastructures (e.g., multimodal sensors and smart devices) that are deployed to supervise a person's health condition, the traditional healthcare system is being revolutionized toward knowledge-centric connected healthcare (KCCH), where persons will take their own responsibility for their healthcare in a knowledge-centric way. Due to the volume, velocity, and variety of healthcare supervision data generated by these healthcare infrastructures, an urgent and strategic issue is how to efficiently process a person's healthcare supervision data with the right knowledge of the right guardians (e.g., relatives, nurses, and doctors) at the right time. To solve this issue, the naming and routing criterion of medical knowledge is studied. With this offloaded medical knowledge, we propose an edge learning as a service (EdgeLaaS) framework for KCCH to locally process health supervision data. In this framework, edge learning nodes can help the patient choose better advice from the right guardians in real time when some emergencies occur. Two application cases: 1) fast self-help and 2) mobile help pre-calling are studied. Performance evaluations demonstrate the superiority of KCCH and EdgeLaaS, respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
