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低碳视角下基于机器学习的医疗救援中心选址研究

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针对伤员遭受突发灾害又无法获得及时救援这一情形,以医院向救援中心输送物资的距离和时间为研究重点,提出一种低碳视角下的应急医疗救援中心选址模型.首先,以上海市为例,基于机器学习的K-means聚类,将上海市划分成四个子区域,使用直线相连法确定各子区域的初始备选点.其次,使用熵权法,从各子区域的初始备选点中筛选出几个最终备选点.最后,通过考虑运输成本、碳排放成本和晚到惩罚成本,计算各子区域内所有医院到救援中心的总成本,将总成本最小的点确定为各子区域的最优选址点,确保每个医院都能向就近的救援中心提供医疗资源.通过对选址结果进行可行性分析,模型得出的最优选址点可作为上海市后续医疗救援中心选址参考.
Machine Learning-Based Location for Medical Rescue Centers from the Perspective of Low-Carbon
Since the injured can't get timely rescue when they suffer from sudden disasters,a location model of emergency medical rescue centers from the perspective of low-carbon is proposed,focusing on the distance and time of transporting medical supplies from hospitals to rescue centers.First,taking Shanghai as an example,the K-means clustering algorithm based on machine learning divides Shanghai into four sub-regions,and the straight-line connection method is used to determine the initial alternative sites of each sub-region.Secondly,the entropy weighting method is used to select several final alternative sites from the initial alternative sites in each sub-region.Finally,by considering the transportation cost,carbon emission cost and late arrival penalty cost,the total cost of all hospitals in each sub-region to the rescue center is calculated,and the site with the minimum total cost is determined as the optimal location site of each sub-region,to ensure that each hospital can provide medical resources to the nearest rescue center.Through the feasibility analysis of the optimal location site,the research result can be used as a reference for subsequent medical rescue center location in Shanghai.

medical rescue center locationmachine learningentropy methodcost of carbon emissionslate arrival penalty cost

彭舒悦、刘勤明、李佳翔

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上海理工大学 上海 200093

医疗救援中心选址 机器学习 熵权法 碳排放成本 晚到惩罚成本

国家自然科学基金国家自然科学基金上海市自然科学基金教育部人文社会科学研究规划基金上海理工大学科技发展项目2021年上海市大学生创新创业训练计划项目

716320087184000319ZR143560020YJAZH0682020KJFZ038SH2021078

2024

10.14071/j.1008-8105(2023)-3033

电子科技大学学报(社会科学版)
电子科技大学

电子科技大学学报(社会科学版)

CHSSCD
影响因子：0.386
ISSN：1008-8105
年,卷(期)：2024.26(2)
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