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Parallel Bayesian Network Modelling for Pervasive Health Monitoring System

机译：普适健康监测系统的并行贝叶斯网络建模

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摘要

Nowadays patients are experiencing pervasive health monitoring service. In order to improve the quality of service, managers and clinicians need to analyze plentiful data collected by medical information system. In this way, because they can acquire useful knowledge, wise decision will be made and the treatment and prevention will become more effective. Since Heart Disease (HD) is considered as one of the main reasons of death for adults, heart disease analysis deserves more attention. This paper uses Bayesian networks to analyze heart disease and presents a method to conform the sequence of network nodes from sample dataset. This method overcomes the limitation of traditional algorithms, which require experts of medical field give the order of network nodes. In addition, in order to shorten the analysis time, a parallel optimization technique is adopted to accelerate the establishment of HD diagnosis model over large amounts of data. Experiments show that the proposed method can improve the accuracy of the modeling and shorten the modeling time to some extent.
机译：如今，患者正在接受广泛的健康监测服务。为了提高服务质量，管理人员和临床医生需要分析由医疗信息系统收集的大量数据。这样，由于他们可以获得有用的知识，因此将做出明智的决定，治疗和预防将变得更加有效。由于心脏病（HD）被认为是成年人死亡的主要原因之一，因此心脏病分析应引起更多关注。本文使用贝叶斯网络对心脏病进行分析，并提出了一种方法来协调样本数据集中网络节点的顺序。该方法克服了传统算法的局限性，传统算法要求医学领域的专家给出网络节点的顺序。另外，为了缩短分析时间，采用并行优化技术来加速对大量数据的高清诊断模型的建立。实验表明，该方法可以提高建模精度，并在一定程度上缩短建模时间。

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