使用贝叶斯网络模型探索与高血压复杂的心脏病发作相关的因素:基于中国健康与退休纵向研究的研究
Haifen Zhang1, Xiaotong Zhang2, Xiaodong Yao1
1Department of General Practice, Shanxi Provincial People's Hospital, The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan, China.
Frontiers in public health
|October 2, 2023
概括
这项研究使用贝叶斯网络来分析与心脏病发作结合高血压 (HAH) 相关的因素. 关键的发现揭示了与HAH的年龄,睡眠,体力活动和自我报告的健康之间的直接联系.
科学领域:
- 医疗信息学 医疗信息学
- 心血管健康 心血管健康
- 流行病学 流行病学
背景情况:
- 贝叶斯网络 (BNs) 为分析复杂疾病因素提供了一个强大的框架.
- 有限的研究专门应用了BN来理解心脏病发作与高血压 (HAH) 结合.
- 这项研究解决了对HAH多因素性质更深入理解的需求.
研究的目的:
- 使用贝叶斯网络探索与高血压 (HAH) 结合的心脏病发作相关因素的复杂网络.
- 为 HAH 风险评估建立贝叶斯推理模型.
- 为HAH预防和治疗策略提供科学基础.
主要方法:
- 利用了2018年中国健康与退休长度调查 (CHARLS) 的数据.
- 用于特征选择的弹性网,以确定影响HAH的关键变量.
- 使用塔布算法构建贝叶斯网络模型,用于结构学习和参数学习的最大概率估计.
主要成果:
- 分析了来自19752人的数据,57.6%的人患有HAH.
- 确定了HAH与年龄,睡眠时间,体力活动和自我报告的健康之间的直接关联.
- 揭示了间接关系:教育水平和日常生活的工具活动 (IADL) 通过身体活动影响HAH; IADL和日常生活的基本活动 (BADL) 通过自我报告的健康影响HAH.
结论:
- 贝叶斯网络有效地可视化了HAH及其贡献因素之间的复杂相互关系.
- 开发的BNs允许对HAH进行风险推理,与临床实践保持一致.
- 这种方法显示出在临床环境中用于HAH管理的重大应用潜力.
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