使用贝叶斯网络与禁忌算法来探索与慢性病与精神疾病相关的因素:一个横截面研究
Xiaoli Yuan1, Wenzhu Song2, Yaheng Li3
1Department of Nephrology, Shanxi Provincial People's Hospital (Fifth Hospital) of Shanxi Medical University, Taiyuan 030012, China.
Mathematical biosciences and engineering : MBE
|November 3, 2023
概括
贝叶斯网络揭示了影响慢性病与精神疾病 (KDMI) 的关键因素. 了解这些关系有助于制定KDMI预防和治疗策略.
科学领域:
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
- 流行病学 流行病学
背景情况:
- 慢性病与精神疾病 (KDMI) 带来了复杂的挑战.
- 贝叶斯网络 (BNs) 为分析多因素疾病提供了一个潜在的框架.
- 有限的研究已经探索了使用BN的KDMI.
研究的目的:
- 调查与KDMI相关的复杂因素网络.
- 在KDMI风险预测中应用贝叶斯推理.
- 为KDMI预防和治疗提供科学基础.
主要方法:
- 利用了来自CHARLS 2018纵向调查的数据.
- 使用随机森林用于缺失值赋值和弹性网用于变量选择.
- 使用 tabu 算法构建贝叶斯网络,在倾向得分匹配后对结构和参数的最大概率估计进行结构和最大概率估计.
主要成果:
- 从18个初始变量中,确定了与KDMI相关的11个显著变量.
- 最终的BN模型包括12个节点和24个定向边缘.
- 糖尿病,体力活动,教育,睡眠,社交活动,自我报告的健康状况和资产是直接因素;性别,年龄,居住地和互联网接入是间接因素.
结论:
- 贝叶斯网络在KDMI中有效地模拟复杂的关系.
- 鉴定的因素为KDMI预防和干预提供了目标.
- BNs显示了KDMI风险因素检测和预测的巨大潜力.
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