用动态因果模型分析睡眠呼吸问题，发现性别年龄差异影响关键因果关系。
Dynamic Structural Causal Modeling for Sleep
- 基于105例家庭睡眠检测数据，用改进算法构建动态因果图
- 发现所有人群都存在呼吸暂停与缺氧的持续关联，其他关系因人而异
- 适合研究睡眠障碍机制或个性化干预的临床医生和研究人员
睡眠呼吸障碍的因果动态复杂且在不同人群间差异显著，制约了精准干预的发展。本研究基于105例家庭睡眠呼吸监测（HSAT）记录，利用PCMCI+算法对分段分数变量建模，结合领域知识进行边排除，并通过自助聚合缓解小样本问题，揭示了性别与年龄亚组间的系统性因果结构差异。结果表明，时间自依赖性和呼吸暂停-低氧关系在所有群体中均持续存在，而其他因果关系则表现出显著差异。
原文摘要 · Abstract (English)
The causal dynamics of sleep-disordered breathing are complex and vary across patient populations, hindering the development of targeted interventions. We learn dynamic causal graphs of sleep-disordered breathing from Home Sleep Apnea Test (HSAT) recordings, revealing systematic differences in causal structure across sex and age subcohorts. We do so using the PCMCI+ algorithm on windowed fractional variables derived from 105 HSAT recordings, exploiting domain knowledge via edge blacklisting and employing bootstrap aggregation to address small subcohort sizes. The learned graphs show that temporal self-dependencies and the apnea-desaturation relationship persist across all cohorts, while other relationships vary substantially.
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