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

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.

7 pages. Accepted for publication in IEEE Systems, Man, and Cybernetics Letters

Machine Learning (cs.LG)

arXiv:2608.19578 [cs.LG]

(or arXiv:2608.19578v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.19578 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.1109/LSMC.2026.3707147 Focus to learn more DOI(s) linking to related resources

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