用家用呼吸机数据提前预测慢阻肺急性加重，给出风险和发生时间。
A Two-Stage Time-Aware Transformer for Short-Horizon AECOPD Risk Prediction
- 分两阶段处理呼吸机近7天原始波形，先判风险再估时间。
- 分类F1达0.91，预测时间误差仅1.00天，相关性达0.76。
- 适合居家慢阻肺患者管理，提供临床可行动的预警窗口。
慢性阻塞性肺病急性加重（AECOPD）进展迅速，及时预测具有重要临床意义。现有机器学习方法多依赖间断采集的临床变量，存在延迟，难以用于家庭监测。家用呼吸机可提供日常使用中的近连续呼吸状态记录。然而现有基于呼吸机的方法或压缩波形为人工特征，或仅关注二分类风险，无法确定事件发生时间。本文提出一种两阶段框架，直接处理最近七天的呼吸压力与流量原始波形。第一阶段分类模型识别高风险患者，第二阶段回归模型估计距严重加重事件剩余天数。实验表明，该框架在风险分类与时间预测上均优于传统基线模型：所选第一阶段分类器取得F1=0.91，第二阶段回归模型实现RMSE=1.00天、R²=0.76，使临床医生获得早期预警与可操作的提前量。
原文摘要 · Abstract (English)
Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) can worsen rapidly, making timely prediction a clinical priority. Most existing machine learning approaches rely on episodically collected clinical variables, introducing delays that limit their practical utility in home monitoring settings. Home ventilators offer a lower-latency alternative, producing a near-continuous record of respiratory status during daily use. However existing ventilator-based approaches either compress the waveform into handcrafted features or focus primarily on binary risk classification, leaving the timing of an impending event unresolved. In this paper, we present a two-stage framework that operates directly on raw pressure and flow waveforms from the most recent seven days of home ventilator use. The first-stage classification model identifies patients at high risk of a severe exacerbation. The second-stage regression model then estimates how many days remain before the event occurs. Our experimental results demonstrate that the two-stage model outperforms traditional baseline models on both risk classification and time-to-event estimation, with our selected Stage 1 classifier achieving F1 = 0.91 and our Stage 2 regression model achieving RMSE = 1.00 days and R^2 = 0.76, giving clinicians both an early warning and actionable lead time before a severe exacerbation occurs.
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