用于短期AECOPD风险预测的两阶段时间感知Transformer
A Two-Stage Time-Aware Transformer for Short-Horizon AECOPD Risk Prediction
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中文总结 AI 辅助
该研究针对AECOPD预测的延迟问题，提出两阶段时间感知Transformer模型，直接处理家用呼吸机原始波形，在风险分类和时间估计上优于基线，为临床提供预警。
中文摘要 AI 辅助
慢性阻塞性肺疾病急性加重（AECOPD）可快速恶化，及时预测是临床重点。多数现有机器学习方法依赖间歇性采集的临床变量，存在延迟，限制了其在家庭监测场景的实用性。家用呼吸机提供了更低延迟的替代方案，可在日常使用中产生近连续的呼吸状态记录，但现有基于呼吸机的方法要么将波形压缩为手工特征，要么主要关注二分类风险预测，未解决事件发生的时间问题。本文提出一种两阶段框架，直接处理家用呼吸机最近7天使用产生的原始压力和流量波形：第一阶段分类模型识别处于严重加重高风险的患者；第二阶段回归模型估计距事件发生的剩余天数。实验结果表明，该两阶段模型在风险分类和事件时间估计上均优于传统基线模型，所选第一阶段分类器F1值达0.91，第二阶段回归模型RMSE为1.00天、R²为0.76，为临床医生提供了严重加重前的早期预警和可操作的前置时间。
英文摘要
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.
发表机构
- Monmouth University（蒙茅斯大学）
- Changzhou Yaoyuanxing Electronic Technology Co., Ltd.（常州遥远兴电子科技有限公司）
机构由 AI 辅助整理，请以论文原文为准。
