Abstract
Health resource planning is an important means for the government to adjust resource allocation and achieve fair and efficient development of health. Its core is the balanced allocation of health resources. In order to solve the problem of low accuracy of health resource demand prediction, an analysis method of health resource allocation equilibrium based on improved machine learning was designed. Using the two-step mobile search method, combined with the Gaussian distance attenuation function and the search threshold set according to the classification of medical facilities, the spatial accessibility of weekday medical services is calculated. Based on the improved machine learning, the demand for health resources is predicted, and the change of resource characteristics and relevant policy variables leads to the change of health resource demand. According to the prediction results of the demand for health care resources, a supply-demand coordination model is constructed to measure the degree of coupling and coordination and the level of hierarchy. Lorentz curve was used to quantify the accessibility distribution of medical resources, Gini coefficient and global Moran index were calculated, and the equilibrium of health resource allocation was analyzed. The results show that this method can accurately predict the demand for health resources, and get the analysis results of allocation balance, which is conducive to the overall integration of medical resources.
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Xiamen Institute of Technology 2021 School-level Young and Middle-aged Scientific Research Fund Project: Binhai Nuclear Power Plant Disaster-Causing Organisms—Detection and Identification of Haitigua, Project No. 6
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© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Wang, Y., Li, H. (2023). Analysis on the Balance of Health Care Resource Allocation Based on Improved Machine Learning. In: Wang, S. (eds) IoT and Big Data Technologies for Health Care. IoTCare 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 501. Springer, Cham. https://doi.org/10.1007/978-3-031-33545-7_8
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DOI: https://doi.org/10.1007/978-3-031-33545-7_8
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