View in EDS

Development of an artificial intelligence prediction model for moderate-to-severe COPD exacerbations using continuous multiple unobtrusive sensors: protocol of a multicentre prospective observational study.

Saved in:
Bibliographic Details
Title: Development of an artificial intelligence prediction model for moderate-to-severe COPD exacerbations using continuous multiple unobtrusive sensors: protocol of a multicentre prospective observational study.
Authors: Vásquez-Andrade R; Barcelona Institute for Global Health, Barcelona, Spain.; Universitat Pompeu Fabra, Barcelona, Spain., Alcaraz-Serrano V; Barcelona Institute for Global Health, Barcelona, Spain.; Universitat Pompeu Fabra, Barcelona, Spain., Buekers J; Barcelona Institute for Global Health, Barcelona, Spain.; Universitat Pompeu Fabra, Barcelona, Spain., Bufano P; Institute of Clinical Physiology, National Research Council, Pisa, Italy., Laurino M; Institute of Clinical Physiology, National Research Council, Pisa, Italy., Celi A; Department of Surgical, Medical, Molecular and Critical Area Pathology, University of Pisa, Pisa, Italy., Watz H; Velocity Clinical Research Germany, Ahrensburg, Germany.; Airway Research Center North (ARCN), German Center for Lung Research (DZL), University of Lübeck, Lübeck, Germany., Gea J; Servei de Pneumologia, Hospital del Mar, Hospital del Mar Research Institute, Barcelona, Spain.; MELIS Department, Universitat Pompeu Fabra, Barcelona, Spain., Carbonaro N; Department of Information Engineering, University of Pisa, Pisa, Italy., Tognetti A; Department of Information Engineering, University of Pisa, Pisa, Italy., Garcia-Aymerich J; Barcelona Institute for Global Health, Barcelona, Spain judith.garcia@isglobal.org.; Universitat Pompeu Fabra, Barcelona, Spain.
Source: BMJ open respiratory research [BMJ Open Respir Res] 2026 Jun 04; Vol. 13 (1). Date of Electronic Publication: 2026 Jun 04.
Publication Type: Clinical Trial Protocol; Journal Article
Journal Info: Publisher: BMJ Publishing Group Ltd & British Thoracic Society Country of Publication: England NLM ID: 101638061 Publication Model: Electronic Cited Medium: Internet ISSN: 2052-4439 (Electronic) Linking ISSN: 20524439 NLM ISO Abbreviation: BMJ Open Respir Res Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2052-4439
DOI:10.1136/bmjresp-2025-003942