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.

翻译：早期预测SARS-CoV-2感染后急性期后遗症（PASC）的严重程度是女性健康面临的关键挑战，尤其是考虑到PASC与更年期等常见激素变化之间存在诊断重叠。识别并解释这些混杂因素对于准确的长期轨迹预测至关重要。我们基于美国国立卫生研究院（NIH）的RECOVER数据集，开展了一项涉及1155名女性（平均年龄61岁）的回顾性研究。通过整合静态临床特征与为期四周的纵向可穿戴设备数据（监测心脏活动和睡眠），我们构建了一个基于大语言模型的因果网络，用于预测未来的PASC评分。我们的框架在临床严重程度预测中达到了86.7%的精确度。因果归因分析表明，该模型能够区分活动性病理与基线噪声：呼吸困难和不适等直接指标达到了最大显著性（1.00），而更年期和糖尿病等混杂因素的显著性评分成功抑制在0.27以下。