Toward Drift-Aware Multimodal Early Warning for Acute Exacerbations of Chronic Obstructive Pulmonary Disease

The clinical management of acute exacerbations of chronic obstructive pulmonary disease (AECOPD) is increasingly moving from reactive treatment to proactive early warning. However, existing environmental forecasting approaches remain constrained by heterogeneous exposure-response relationships, limited AECOPD-specific evidence for digital behavioral surveillance, and vulnerability to concept drift under nonstationary social and health care conditions. This viewpoint argues for a drift-aware multimodal early warning research agenda that integrates environmental exposure data, candidate digital behavioral signals, and routinely collected clinical burden indicators. Drawing on representative literature from environmental epidemiology, respiratory medicine, digital epidemiology, and medical informatics, we discuss current methodological challenges and potential directions for future AECOPD surveillance. Particular attention is paid to multimodal data integration, concept drift, adaptive state-space modeling, regime-aware handling of structural breaks, and equity-related challenges associated with digital behavioral data. Rather than presenting a validated forecasting system, we outline conceptual design considerations for future AECOPD-specific studies, including multimodal data fusion, state-space adaptive updating, drift subtype diagnosis, and subgroup-aware validation strategies. We also highlight important evidence gaps, particularly regarding the use of internet search queries and social media signals for AECOPD prediction. Drift-aware multimodal surveillance represents a promising direction for future AECOPD early warning, but substantial methodological, clinical, and implementation challenges remain. Future research should prioritize disease-specific validation, transparent evaluation of adaptive forecasting methods, and equitable deployment across populations with differing levels of digital access and health care resources.