School of Electronics Engineering, Vellore Institute of Technology, Chennai 600127, India.
Sensors (Basel). 2020 Apr 10;20(7):2153. doi: 10.3390/s20072153.
This paper is a collection of telemedicine techniques used by wireless body area networks (WBANs) for emergency conditions. Furthermore, Bayes' theorem is proposed for predicting emergency conditions. With prior knowledge, the posterior probability can be found along with the observed evidence. The probability of sending emergency messages can be determined using Bayes' theorem with the likelihood evidence. It can be viewed as medical decision-making, since diagnosis conditions such as emergency monitoring, delay-sensitive monitoring, and general monitoring are analyzed with its network characteristics, including data rate, cost, packet loss rate, latency, and jitter. This paper explains the network model with 16 variables, with one describing immediate consultation, as well as another three describing emergency monitoring, delay-sensitive monitoring, and general monitoring. The remaining 12 variables are observations related to latency, cost, packet loss rate, data rate, and jitter.
本文是一篇关于无线体域网（WBANs）在紧急情况下使用的远程医疗技术的论文。此外，还提出了贝叶斯定理来预测紧急情况。根据先验知识，可以找到后验概率以及观察到的证据。可以使用贝叶斯定理和似然证据来确定发送紧急消息的概率。这可以看作是医疗决策，因为对紧急监测、延迟敏感监测和一般监测等诊断条件进行了分析，同时考虑了其网络特征，包括数据速率、成本、丢包率、延迟和抖动。本文用 16 个变量解释了网络模型，其中一个变量描述了即时咨询，另外三个变量描述了紧急监测、延迟敏感监测和一般监测。其余 12 个变量是与延迟、成本、丢包率、数据速率和抖动有关的观察值。
