School of Computers, Information and Mathematical Sciences, BSA Crescent Institute of Science and Technology, Chennai, India.
J Med Syst. 2019 Jul 1;43(8):257. doi: 10.1007/s10916-019-1392-4.
Telemedicine research improves the connectivity of remote patients and doctors. Researchers are focused on data optimization and processing over a predefined channel of communication under a depictive low QoS. In this paper a consolidated representation of telemedicine infrastructure of modern topological arrangement is represented and validated. The infrastructure is aided with Multiple Objective Optimized Medical dataset (MooM) processing and a channel optimizing TelMED protocol designed exclusively for remote medicine dataset transmission and processing. The proposed infrastructure provides an application oriented approach towards Electronics health records (EHR) creation and updating over edge computation. The focus of this article is to achieve higher order of Quality of Service (QoS) and Quality of Data (QoD) compared to typical communication channels algorithms for processing of medical data sample. Typically the proposed technique results are achieved to discuss in MooM dataset processing and TelMED channel optimization sessions and a resulting improvement is discussed with a comparison of each MooM dataset in reverse processing towards server end of diagnosis and a consolidated QoS is retrieved for proposed infrastructure.
远程医疗研究提高了远程患者和医生之间的连接性。研究人员专注于在描述性低 QoS 下通过预定义的通信通道进行数据优化和处理。本文代表并验证了现代拓扑结构的远程医疗基础设施的综合表示。该基础设施通过多目标优化医疗数据集 (MooM) 处理和专门为远程医疗数据集传输和处理设计的信道优化 TelMED 协议提供支持。所提出的基础设施提供了一种面向应用的方法，用于在边缘计算中创建和更新电子健康记录 (EHR)。本文的重点是与处理医疗数据样本的典型通信通道算法相比，实现更高的服务质量 (QoS) 和数据质量 (QoD) 等级。通常，所提出的技术结果将在 MooM 数据集处理和 TelMED 信道优化会议中进行讨论，并通过对每个 MooM 数据集进行反向处理到服务器端诊断的比较来讨论改进结果，并为提出的基础设施检索到综合的 QoS。
