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社会决定因素对心血管死亡率的影响:印第安纳州邮政编码级分析
Ria Treesa Raju1, Pallavi Telu1, Thejomayi Malepati1
1Luddy School of Informatics, Computing and Engineering, Indiana University, Indianapolis, Indiana, USA.
Studies in health technology and informatics
|August 8, 2025
概括
这项研究开发了一个地理空间框架,使用健康的社会决定因素 (SDoH) 预测心血管疾病 (CVD) 死亡率. 教育,种族,收入和获得医疗保健是关键预测因素,使得有针对性的公共卫生干预措施成为可能.
科学领域:
- 医疗信息学 医疗信息学
- 地理空间分析的研究.
- 公共卫生 公共卫生
背景情况:
- 心血管疾病 (CVD) 是全球主要的死亡原因.
- 将健康的社会决定因素 (SDoH) 整合到对心血管疾病的预测健康信息学模型中是有限的.
- 对心血管疾病死亡率的局部预测对于解决健康差异至关重要.
研究的目的:
- 开发一个可扩展的地理空间框架来预测印第安纳州邮政编码级别的CVD死亡率.
- 整合各种数据来源,包括社会经济,环境和医疗保健基础设施指标.
- 为了确定针对性干预措施的CVD死亡率的关键SDoH预测因素.
主要方法:
- 利用了一个地理空间框架,整合了美国人口普查局,OpenStreetMap,Zillow和印第安纳州卫生部 (2015-2022) 的数据.
- 采用数据预处理技术,包括空间规范化,强大的缩放和人口调整的特征工程.
- 测试和比较机器学习模型 (随机森林,XGBoost,FastAI Tabular),其中XGBoost表现出卓越的性能.
主要成果:
- 在预测心血管疾病死亡率方面,XGBoost模型实现了高精度 (R2 = 0.9863;MAE = 2.29).
- 在SHAP分析中,教育,种族,收入和医疗保健的准入被确定为重要的SDoH预测因素.
- 该框架证明了局部,基于SDoH的死亡率预测的可复制性.
结论:
- 该研究提出了一个新的,可复制的管道,用于使用地理空间和SDoH数据进行局部CVD死亡率预测.
- 调查结果强调了将SDoH和地理空间数据整合到健康信息学中,以解决健康差异的重要性.
- 开发的框架可以指导社区一级的有针对性的公共卫生干预措施.
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