计算和预测血糖水平的非随机元差分进化

T. Koutny, A. D. Cioppa, I. D. Falco, E. Tarantino, U. Scafuri, M. Krcma
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引用次数: 3

摘要

在构建医疗设备时,生理模型可以改善交付的医疗保健。这样的模型包括许多参数。分析方法确定模型参数,而进化算法可以进一步改进模型参数。由于进化算法是在随机数生成器的基础上设计的,其结果是不确定的。这引起了对它们在医疗器械中的适用性的关注。医疗设备算法必须产生具有最低保证精度的输出。因此,我们将去随机化序列应用于元差分进化,而不是使用随机数生成器。最后,我们设计了一种基于非随机化序列缩放的优化方法,作为元差分进化的替代方法。作为实验设置,我们预测葡萄糖水平信号覆盖了葡萄糖运输生理滞后导致的葡萄糖监测信号的盲窗。完全去随机化的差异进化与完全非确定性的差异进化具有相同的准确性和精密度。它们产生了93%的葡萄糖水平,相对误差小于或等于15%。
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De–randomized Meta-Differential Evolution for Calculating and Predicting Glucose Levels
A physiological model improves delivered healthcare, when constructing a medical device. Such a model comprises a number of parameters. While an analytical method determines model parameters, an evolutionary algorithm can improve them further. As evolutionary algorithms were designed on top of random-number generators, their results are not deterministic. This raises a concern about their applicability to medical devices. Medical-device algorithm must produce an output with a minimum guaranteed accuracy. Therefore, we applied de-randomized sequences to Meta-Differential Evolution instead of using a random-number generator. Eventually, we designed an optimization method based on zooming with derandomized sequences as an alternative to the Meta-Differential Evolution. As the experimental setup, we predicted glucose-level signal to cover a blind window of glucose-monitoring signal that results from a physiological lag in glucose transportation. Completely de-randomized differential evolution exhibited the same accuracy and precision as completely non-deterministic differential evolution. They produced 93% of glucose levels with relative error less than or equal to 15%.
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