基于数据的预测心血管和脑血管疾病在全国性的研究研究
Sehyun Kim1, Beomsang Ryu2, Mingee Choi3
1Department of Economics, Seoul National University College of Social Science, Gwanak-gu, Seoul, 08826, Republic of Korea.
Scientific reports
|July 2, 2025
概括
预测心脑血管疾病 (CCVD) 发生率至关重要. 用LASSO选择的变量进行后勤回归模型显示出强大的预测性能,特别是在高风险人群中.
科学领域:
- 公共卫生 公共卫生
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
背景情况:
- 心血管和脑血管疾病 (CCVD) 构成了全球严重的健康负担.
- 有效的预防和早期管理策略依赖于准确的风险预测模型.
- 医疗保健中的大数据分析能够开发出复杂的预测工具.
研究的目的:
- 用医疗保险大数据预测心脑血管疾病 (CCVD) 的发生率.
- 为了比较不同初始风险水平的各种预测模型的性能.
- 为CCVD预防和管理提供医疗保健政策制定信息.
主要方法:
- 来自国家医疗保险服务 (2002-2019) 的大型数据集 (410,859人) 的分析.
- 部署后勤回归模型与惩罚条款 (例如,LASSO) 进行变量选择和预测.
- 使用诸如接收器运行特征曲线 (AUC) 下面面积等指标评估模型性能.
主要成果:
- 将LASSO选择的变量纳入的后勤回归模型显示了CCVD发病率的优异预测性能.
- 对于具有较高发病率和较高初始风险水平的样本,模型性能有所改善.
- 虽然LASSO模型表现最好,但模型之间的差异在统计学上并不显著.
结论:
- 预测模型,特别是LASSO的后勤回归,可以有效地使用国家医疗保险数据预测CCVD发病率.
- 为不同的风险层量身定制模型可以提高预测准确度.
- 这些发现支持针对高风险人群制定有针对性的医疗保健政策和干预措施.
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