对于缺失的医疗保健系统,使用顺序热甲板推算方法 人口健康研究数据
Ella A Chrenka1, Steven P Dehmer1, Michael V Maciosek1
1HealthPartners Institute, Bloomington, MN.
Medical care
|March 28, 2024
概括
顺序热甲板归算 (HDI) 有效地处理缺少的电子病历 (EMR) 数据,用于人口健康研究. 这种方法创建了一个单一的,完整的数据集,用于风险建模,保存数据分布和相关性.
科学领域:
- 医疗信息学 医疗信息学
- 人口健康研究 人口健康研究
- 生物统计学 生物统计学
背景情况:
- 电子病历 (EMR) 数据对人口健康研究有价值.
- 在EMR数据中缺失的高率阻碍了准确的人口风险模型的开发.
- 传统的方法,如完整的案例分析和多重归算,可能无法产生适合所有分析的单一完整数据集.
研究的目的:
- 展示一个连续的热甲板归算 (HDI) 程序来解决EMR数据中缺失的心脏代谢指标.
- 评估单个变量和复合风险评分的顺序性HDI的表现.
- 评估顺序性人类健康指数是否适用于为人口健康倡议创建单一,完整的分析数据集.
主要方法:
- 使用了来自两个大型城市医院的心脏代谢测量数据集.
- 模拟失踪被引入,以创建一个基准数据集.
- 顺序热甲板归算 (HDI) 应用于EMR数据,并计算了动脉样硬化心血管疾病风险得分.
主要成果:
- 在567,841名患者中,有65%的患者至少缺少一个心脏代谢指标.
- 顺序HDI成功生成了可变分布和风险得分,反映了模拟数据,同时保持了相关性.
- 以年龄和性别分层的假设风险得分是合理的,反映了预期的人口模式.
结论:
- 顺序热甲板归算 (HDI) 是一种适合处理电子医疗记录 (EMR) 数据中的多变量缺失的方法.
- 这种方法提供了一种简单而有效的计算方法来解决缺失的EMR数据.
- 顺序性HDI通过产生单一,完整的分析数据集,促进了人口健康研究.
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