用因果解耦预测女性长新冠轨迹，区分病理性症状与激素干扰
Predicting Trajectories of Long COVID in Adult Women: The Critical Role of Causal Disentanglement
- 基于大语言模型构建因果网络，融合静态临床与可穿戴设备数据
- 临床严重度预测精度达86.7%，病理性症状显著性最高（1.00）
- 有效抑制更年期等干扰因素影响，适合关注女性长期健康的研究者
早期预测新冠病毒后遗症（PASC）严重程度对女性健康至关重要，尤其因PASC与更年期等激素变化存在诊断重叠。本研究基于美国国立卫生研究院RECOVER队列中1,155名女性（平均年龄61岁）的回顾性数据，整合静态临床特征与连续四周的可穿戴设备数据（监测心率与睡眠），构建基于大语言模型的因果网络以预测未来PASC评分。该框架在临床严重度预测上达到86.7%的准确率。因果归因分析表明，模型能有效区分真实病理信号与基线噪声：如呼吸困难和乏力等直接指标达到最大显著性（1.00），而更年期、糖尿病等混杂因素显著性均低于0.27，实现精准分离。
原文摘要 · Abstract (English)
Early prediction of Post-Acute Sequelae of SARS-CoV-2 severity is a critical challenge for women's health, particularly given the diagnostic overlap between PASC and common hormonal transitions such as menopause. Identifying and accounting for these confounding factors is essential for accurate long-term trajectory prediction. We conducted a retrospective study of 1,155 women (mean age 61) from the NIH RECOVER dataset. By integrating static clinical profiles with four weeks of longitudinal wearable data (monitoring cardiac activity and sleep), we developed a causal network based on a Large Language Model to predict future PASC scores. Our framework achieved a precision of 86.7\% in clinical severity prediction. Our causal attribution analysis demonstrate the model's ability to differentiate between active pathology and baseline noise: direct indicators such as breathlessness and malaise reached maximum saliency (1.00), while confounding factors like menopause and diabetes were successfully suppressed with saliency scores below 0.27.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。