Evidence map›Paper›PMID 42574082›Full record

ArticleJMIR medical informatics2026

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

Haoran Li, Shuaihua Fan, Xiaojuan Li, Nina Liu, Huaiqing Qi, Jun Guo

Abstract read
In one paragraph

Article in JMIR medical informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Haoran LiDepartment of Respiratory and Critical Care Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, 168 Litang Road, Changping District, Beijing, China, +86 13681573493.ORCID 0009-0002-0457-3685
Shuaihua FanDepartment of Respiratory and Critical Care Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, 168 Litang Road, Changping District, Beijing, China, +86 13681573493.ORCID 0009-0004-9643-8863
Xiaojuan LiYichang Central People's Hospital, The First College of Clinical Medical Science, China Three Gorges University, Yichang, Hubei, China.ORCID 0009-0007-5782-7665
Nina LiuDepartment of Respiratory and Critical Care Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, 168 Litang Road, Changping District, Beijing, China, +86 13681573493.ORCID 0009-0004-8634-6308
Huaiqing QiTsinghua University, Beijing, China.ORCID 0009-0007-2504-0518
Jun GuoDepartment of Respiratory and Critical Care Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, 168 Litang Road, Changping District, Beijing, China, +86 13681573493.ORCID 0000-0002-7374-2465

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unlabelled: 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.

Indexed as

Pulmonary Disease, Chronic ObstructiveDisease ProgressionEarly DiagnosisHumansacute exacerbationchronic obstructive pulmonary diseaseconcept driftCOPDearly warning systemenvironmental exposure

Identifiers

PMID42574082
PMCPMC13455723

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.