混合智能远程医疗监测和预测系统

U. Umoh, Imo J. Eyoh, V. Murugesan, A. Abayomi, S. Udoh
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引用次数: 2

摘要

卫生保健系统需要克服与心血管疾病相关的高死亡率,并通过使用定量和定性的决策支持模型来改善患者的健康。然而,现有的模型强调数学过程,只适合分析定量决策变量,而没有考虑到一些相关的定性决策变量,这些变量不能简单地量化。为了解决这一问题,区间2型模糊逻辑(IT2FL)和花授粉算法(FPA)等模型被分离使用。IT2FL是T2FL的简化版,降低了计算复杂度,增加了设计自由度,但不能自然地达到它所使用的决策规则。FPA是一种基于授粉过程的生物启发方法,由开花植物执行,具有学习、概括和处理大量可测量数据的能力,但它无法描述它是如何做出决定的。混合智能IT2FL-FPA系统可以克服单个方法的限制,增强其鲁棒性,以应对医疗保健数据。本文采用IT2FL和FPA技术开发了一种混合智能远程医疗监测和预测系统。本文的主要目标是找到IT2FL的最佳隶属函数参数,以获得最优解。利用FPA技术找到了用于it2fls的MFs的最佳参数。针对监测和预测问题,作者测试了两个数据集,即心血管疾病患者的临床数据集和用于休克水平监测和预测的实时数据集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hybrid intelligent telemedical monitoring and predictive systems
Healthcare systems need to overcome the high mortality rate associated with cardiovascular disease and improve patients’ health by using decision support models that are both quantitative and qualitative. However, existing models emphasize mathematical procedures, which are only good for analyzing quantitative decision variables and have failed to consider several relevant qualitative decision variables which cannot be simply quantified. In solving this problem, some models such as interval type-2 fuzzy logic (IT2FL) and flower pollination algorithm (FPA) have been used in isolation. IT2FL is a simplified version of T2FL, with a reduced computation complexity and additional design degrees of freedom, but it cannot naturally achieve the rules it uses in making decisions. FPA is a bio-inspired method based on the process of pollination, executed by the flowering plants, with the ability to learn, generalize and process numerous measurable data, but it is not able to describe how it reaches its decisions. The hybrid intelligent IT2FL-FPA system can conquer the constraints of individual approaches and strengthens their robustness to cope with healthcare data. This work develops a hybrid intelligent telemedical monitoring and predictive system using IT2FL and FPA. The main objective of this paper is to find the best membership functions (MFs) parameters of the IT2FL for an optimal solution. The FPA technique is employed to find the optimal parameters of the MFs used for IT2FLSs. The authors tested two data sets for the monitoring and prediction problems, namely: cardiovascular disease patients’ clinical and real-time datasets for shock-level monitoring and prediction.
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CiteScore
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