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针对人口密集型都市的医疗资源配置缺乏准确性预测作为实时决策依据的难题,利用灰色理论的小样本建模优势进行预测方法拓展优化,建立等维递补灰色预测方法以提高灰区间白色度和淡化灰平面灰度;挖掘灰色生成系数与外部影响因素间的内在映射关联,提出了动态生成系数优化的灰色理论医疗需求预测方法;动态拟合人口总量变化与灰色生成系数以实现预测模型实时重构,解决了传统灰色预测方法的纯样本序列建模局限,显著提高了预测算法的准确性,输出的医疗需求趋势可有力支撑医疗资源配置决策。
Lack of accuracy prediction of medical resource allocation in densely populated metropolis as a real-time decision-making problem, using the advantages of small sample modeling gray theory to expand and optimize the forecasting method, and establishing the equal-dimension gray-scale forecasting method to improve the whiteness and fading Gray-scale gray-scale, Gray correlation coefficient between gray generation factor and external influencing factors, and proposed gray-theory theory of medical demand forecasting method with dynamic generation coefficient optimization. Dynamic fitting of total population change and gray generation coefficient can realize real- It solves the limitation of the pure gray-scale sequence modeling by the traditional gray forecasting method and significantly improves the accuracy of the forecasting algorithm. The output trend of medical demand can strongly support the decision-making of medical resource allocation.