Abstract: The budget serves as a manifestation of a nation's currency, with risks in domains such as public health and natural disasters exerting influence on budget allocation. Utilizing data from 21 prefecture-level cities in Guangdong spanning from 2014 to 2022 as research samples, this paper employs a hybrid artificial intelligence model to investigate the nexus between public health budget allocation and various metrics including population mortality rate, incidence of category A and category B infectious diseases, and per capita regional gross domestic product (GDP). The findings of the research are as follows: (1)Increasing the budget levels for health supervision agencies, emergency public health response, and disease prevention and control institutions is an effective way to reduce population mortality rates. (2)Optimizing the budget allocation for basic public health services, urban community health institutions, and elderly health affairs is crucial for controlling the incidence of Class A and B infectious diseases. (3)Disease prevention and control institutions, emergency response to public health incidents, and blood supply agencies are the cornerstones of safeguarding labor health and economic growth;(4) Notably, a certain degree of goal incongruity exists between curbing the incidence of infectious diseases and bolstering per capita regional GDP, while the objectives of reducing population mortality rates and enhancing GDP exhibit relative alignment.

Key words: Budget Expenditure, Artificial Intelligence, Budget Allocation, Public Health

摘要： 通过将人工智能运用于公共卫生预算分配,本文实证检验了人工智能方法在财政预算支出管理中的有效性。本文以2014—2022年广东21个地级市为研究样本,构建人工智能模型探讨公共卫生预算支出与人口死亡率、甲乙类传染病发病数量和人均地区生产总值等多目标的联系。研究发现:第一,提高卫生监督机构、突发公共卫生应急处理以及疾病预防控制机构预算水平是降低人口死亡率的有效途径;第二,优化基本公共卫生服务、城市社区卫生机构和老龄卫生健康事务的预算分配是控制甲乙类传染病发病数量的关键;第三,疾病预防控制机构、突发公共卫生事件应急处理和采供血机构的预算支出是保障劳动力健康和经济增长的基石;第四,控制传染病发病数量与提高人均地区生产总值存在一定程度的目标冲突,而降低人口死亡率与提高生产总值的目标则较为一致。

关键词: 财政预算支出, 人工智能, 预算分配, 公共卫生