A Two-Stage Time-Aware Transformer for Short-Horizon AECOPD Risk Prediction

2026-08-19

AI Summary

本文提出一种两阶段时间感知Transformer模型，直接处理家用呼吸机的原始压力和流量波形数据，以预测短期内AECOPD的风险及其发生时间。

Abstract

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.

Problem

• AECOPD
• time-aware
• risk prediction
• home ventilator
• waveform

Innovation

• Two-Stage Time-Aware Transformer
• Short-Horizon AECOPD Risk Prediction
• Raw Pressure and Flow Waveforms
• Time-to-Event Estimation