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An integrated model using the Taguchi method and artificial neural network to improve artificial kidney solidification parameters

机译:使用Taguchi方法和人工神经网络的集成模型以改善人工肾脏凝固参数

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Hemodialysis mainly relies on the “artificial kidney,” which plays a very important role in temporarily or permanently substituting for the kidney to carry out the exchange of waste and discharge of water. Nevertheless, a previous study on the artificial kidney has paid little attention to the optimization of factors and levels for reducing the solidification of the artificial kidney during the hemodialysis procedure. Thus, this study proposes an integrated model that uses the Taguchi method, omega formula, and back-propagation network to determine the optimal factors and levels for addressing this issue. First, we collected the recommendations of medical doctors and nursing staff through a small group discussion, and used the Taguchi method to analyze the key factors at different levels. Next, the omega formula was used to convert the analysis results from the Taguchi method to assess the defect rate. Finally, we utilized back-propagation network algorithms to predict the optimal factors and levels for artificial kidney solidification, in order to confirm that the key factors and levels identified can effectively improve the solidification rate of the artificial kidney and thereby enhance the effect of hemodialysis. The research finding proposes the following as the optimal factors and levels for artificial kidney solidification: the amount of anticoagulation should be set at 500 units, the velocity of blood flow at 300?ml/min, the dehydration volume at 2.5?kg, and the vascular access type as autologous blood vessels. We obtained 270 sets of data from the patients of End Stage Renal Disease (ESRD) under the setting of the optimal combination of the factors at different levels; the defect rate of artificial kidney solidification is 12.9%, which is better than the defect rate of 32% in the original experiment. Meanwhile, the patient characteristics for physiological status in BMI, serum calcium, hematocrit, ferritin, and transferrin saturation percentage are improved by this study. This conclusion validates the ability of the proposed model in this study to improve the solidification rate of the artificial kidney, thereby confirming the model’s use as a standard operation procedure in the hemodialysis experiment. The ideas behind and the implications of the proposed model are further discussed in this study.
机译:血液透析主要依靠“人工肾脏”,它在暂时或永久替代肾脏进行废物交换和排水方面起着非常重要的作用。然而,先前关于人造肾脏的研究很少关注在血液透析过程中减少人造肾脏凝固的因素和水平的优化。因此,本研究提出了使用田口方法,欧米伽公式和反向传播网络来确定解决该问题的最佳因素和水平的集成模型。首先,我们通过小组讨论收集了医生和护理人员的建议,并使用田口方法分析了不同级别的关键因素。接下来,使用欧米茄公式转换Taguchi方法的分析结果以评估缺陷率。最后,我们利用反向传播网络算法预测了人工肾脏凝固的最佳因素和水平,以确认确定的关键因素和水平可以有效提高人工肾脏的凝固速度,从而增强血液透析的效果。研究结果提出以下作为人工肾凝固的最佳因素和水平:抗凝量应设定为500个单位,血流速度应设定为300μml/ min,脱水量应设定为2.5μkg,并且血管通路类型为自体血管。在不同水平因素的最佳组合设置下,我们从晚期肾病患者获得了270套数据。人工肾凝固的缺陷率为12.9%,优于原始实验的32%。同时,这项研究改善了患者的BMI生理状况,血清钙,血细胞比容,铁蛋白和转铁蛋白饱和度的特征。这一结论证实了该模型在提高人工肾脏凝固速度方面的能力,从而证实了该模型在血液透析实验中作为标准手术程序的应用。本研究进一步讨论了所提出模型的背后思想和含义。

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