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.

翻译：通过将网络技术与服务引入部署用于监测个人健康状况的医疗基础设施（如多模态传感器和智能设备），传统医疗体系正朝着知识中心型互联医疗（KCCH）变革，使个人能够以知识为中心的方式自主管理健康。由于这些医疗基础设施产生的健康监测数据具有海量性、高速性和多样性特征，如何高效地在恰当时间以正确知识服务于恰当监护人（如亲属、护士和医生）来处理个人健康监测数据，已成为一项紧迫的战略性问题。为解决该问题，本文研究了医疗知识的命名与路由准则。基于这种可卸载的医疗知识，我们提出了一种面向KCCH的边缘学习即服务（EdgeLaaS）框架，用于本地处理健康监测数据。在该框架中，边缘学习节点可在紧急情况发生时，实时帮助患者从恰当的监护人处获取更优建议。本文研究了两个应用案例：1）快速自助救助；2）移动救助预呼叫。性能评估分别证明了KCCH和EdgeLaaS框架的优越性。