发表机构
Marche Polytechnic University; Polytechnic of Turin; University of Molise; Scientific Institute for Hospitalization and Care (INRCA)（马尔凯理工大学; 都灵理工大学; 莫利塞大学; 意大利国家老年care研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用符号回归识别意大利地区医疗保健支出驱动因素，通过多时间窗口建模分析，成功预测私人支出并揭示其驱动因素，但对公共支出预测效果不佳。
AI 中文摘要
研究驱动医疗保健支出动态的因素对于指导政策制定者分配资源和衡量所考虑医疗保健系统的有效性至关重要。这些驱动因素的识别可以通过机器学习技术的使用得到支持，这些技术能够在海量数据中发现隐藏模式。与黑箱方法相比，符号回归（SR）是一种能够识别明确捕获数据内函数关系的解析模型的方法，从而增强可解释性。在本文中，我们展示了使用符号回归来识别意大利地区医疗保健支出的驱动因素。鉴于这一现象的动态性和复杂性，我们基于不同的时间窗口生成了多个模型，使我们能够分析不同时间范围内的驱动因素。除了基于生成模型中变量频率识别主要驱动因素外，我们还对重复出现的子结构进行了研究。结果表明，符号回归能够为私人医疗保健支出生成具有良好预测精度的模型，从而能够对其驱动因素进行可靠分析，但未能为公共医疗保健支出做到这一点。
英文摘要
The study of the factors driving the dynamics of healthcare spending is of paramount importance to guide policymakers in the allocation of resources and to measure the effectiveness of the healthcare system under consideration. The identification of these drivers can be supported by the use of machine learning techniques, which enable the discovery of hidden patterns within vast amounts of data. In contrast to black-box methods, Symbolic Regression (SR) is an approach that allows for the identification of analytical models that explicitly capture the functional relationships within the data, thus enhancing interpretability. In this paper, we present the use of SR for identifying the drivers of healthcare expenditure in Italian regions. Given the dynamic and complex nature of this phenomenon, we generated several models based on distinct temporal windows, enabling us to analyze the drivers across different time horizons. In addition to identifying the main drivers based on variable frequency in the generated models, we also conducted a study on recurring substructures. The results show that SR was able to generate models with a good level of predictive accuracy for private healthcare expenditure, enabling a reliable analysis of its driving factors, but failed to do so for public healthcare expenditure.
