ArticleChronic obstructive pulmonary diseases (Miami, Fla.)2022
Machine Learning Prediction of Progression in Forced Expiratory Volume in 1 Second in the COPDGene® Study.
Article in Chronic obstructive pulmonary diseases (Miami, Fla.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.
- Definitions of early COPD and predictors for disease progression: a systematic review.European respiratory review : an official journal of the European Respiratory Society · 2026Pooled it
- Article
- Prognostic Models for Disease Progression and Outcomes in Chronic Obstructive Pulmonary Disease: A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2025Review
- Leveraging Subjective Parameters and Biomarkers in Machine Learning Models: The Feasibility ofDiagnostics (Basel, Switzerland) · 2025Article
- Estimating rate of lung function change using clinical spirometry data.BMJ open respiratory research · 2024Article
- Identification of factors directly linked to incident chronic obstructive pulmonary disease: A causal graph modeling study.PLoS medicine · 2024Article
- Machine learning for screening of at-risk, mild and moderate COPD patients at risk of FEVFrontiers in physiology · 2023Article
- Predicting COPD Progression in Current and Former Smokers Using a Joint Model for Forced Expiratory Volume in 1 Second and Forced Expiratory Volume in 1 Second to Forced Vital Capacity Ratio.Chronic obstructive pulmonary diseases (Miami, Fla.) · 2022Article
Corrections and comments
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Authors and funding
12 authors at 6 institutions in 1 country.
Funding
Abstract
Background: The heterogeneous nature of chronic obstructive pulmonary disease (COPD) complicates the identification of the predictors of disease progression. We aimed to improve the prediction of disease progression in COPD by using machine learning and incorporating a rich dataset of phenotypic features. Methods: We included 4496 smokers with available data from their enrollment and 5-year follow-up visits in the COPD Genetic Epidemiology (COPDGene Results: Predicting the change in FEV Conclusion: Random forest, along with deep phenotyping, predicts FEV
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Registered trials
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.