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This paper introduces a two-stage framework for predicting acute exacerbations of chronic obstructive pulmonary disease (AECOPD) using raw pressure and flow waveforms from home ventilators. By first classifying high-risk patients and then estimating the time until exacerbation, the model provides timely and actionable insights for clinicians, addressing the limitations of existing episodic data approaches. Experimental results show significant improvements over traditional models, with an F1 score of 0.91 for risk classification and a regression model achieving an RMSE of 1.00 days, enhancing early warning capabilities for patient management.
Clinicians can now receive both an early warning and precise lead time for AECOPD exacerbations, thanks to a novel two-stage model that leverages continuous ventilator data.
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.
