{"slug": "a-two-stage-time-aware-transformer-for-short-horizon-aecopd-risk-prediction", "title": "A Two-Stage Time-Aware Transformer for Short-Horizon AECOPD Risk Prediction", "summary": "A new two-stage time-aware transformer model predicts acute exacerbation of chronic obstructive pulmonary disease (AECOPD) from raw home ventilator waveforms, achieving F1 = 0.91 for risk classification and RMSE = 1.00 days with R^2 = 0.76 for time-to-event estimation. The model, described in an arXiv paper (2608.19578v1), uses the most recent seven days of pressure and flow data to identify high-risk patients and estimate days until a severe exacerbation, offering clinicians actionable lead time.", "body_md": "arXiv:2608.19578v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/a-two-stage-time-aware-transformer-for-short-horizon-aecopd-risk-prediction", "canonical_source": "https://www.machinebrief.com/news/a-two-stage-time-aware-transformer-for-short-horizon-aecopd-ckd2", "published_at": "2026-08-21 04:00:00+00:00", "updated_at": "2026-08-21 04:14:11.491239+00:00", "lang": "en", "topics": ["machine-learning", "artificial-intelligence"], "entities": ["arXiv"], "alternates": {"html": "https://wpnews.pro/news/a-two-stage-time-aware-transformer-for-short-horizon-aecopd-risk-prediction", "markdown": "https://wpnews.pro/news/a-two-stage-time-aware-transformer-for-short-horizon-aecopd-risk-prediction.md", "text": "https://wpnews.pro/news/a-two-stage-time-aware-transformer-for-short-horizon-aecopd-risk-prediction.txt", "jsonld": "https://wpnews.pro/news/a-two-stage-time-aware-transformer-for-short-horizon-aecopd-risk-prediction.jsonld"}}