ArticleIEEE journal of biomedical and health informatics2020
Classification of Exacerbation Frequency in the COPDGene Cohort Using Deep Learning With Deep Belief Networks.
Article in IEEE journal of biomedical and health informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses 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
10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 36 citations in OpenAlex.
- Chest CT in the evaluation of chronic obstructive pulmonary disease: recommendations of Asian Society of Thoracic Radiology.The British journal of radiology · 2026Guideline
- Accuracy of Deep Learning in Diagnosing Chronic Obstructive Pulmonary Disease: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Entropy-regularized dual-stream attention fusion for multi-disease lung classification.Scientific reports · 2026Article
- Development and Validation of an Electronic Health Record Algorithm to Predict the Presence of Chronic Obstructive Pulmonary Disease.International journal of chronic obstructive pulmonary disease · 2026Article
- Prognostic Models for Disease Progression and Outcomes in Chronic Obstructive Pulmonary Disease: A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2025Review
- Multi-channel lung sounds intelligent diagnosis of chronic obstructive pulmonary disease.BMC pulmonary medicine · 2021Article
- Machine Learning and Deep Neural Networks in Thoracic and Cardiovascular Imaging.Journal of thoracic imaging · 2019Review
- Personalized medicine for patients with COPD: where are we?International journal of chronic obstructive pulmonary disease · 2019Review
- Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning.Scientific reports · 2017Article
- Artificial Intelligence in COPD: New Venues to Study a Complex Disease.Barcelona respiratory network reviewsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 5 institutions in 3 countries.
Funding
Abstract
This study aims to develop an automatic classifier based on deep learning for exacerbation frequency in patients with chronic obstructive pulmonary disease (COPD). A three-layer deep belief network (DBN) with two hidden layers and one visible layer was employed to develop classification models and the models' robustness to exacerbation was analyzed. Subjects from the COPDGene cohort were labeled with exacerbation frequency, defined as the number of exacerbation events per year. A total of 10 300 subjects with 361 features each were included in the analysis. After feature selection and parameter optimization, the proposed classification method achieved an accuracy of 91.99%, using a ten-fold cross validation experiment. The analysis of DBN weights showed that there was a good visual spatial relationship between the underlying critical features of different layers. Our findings show that the most sensitive features obtained from the DBN weights are consistent with the consensus showed by clinical rules and standards for COPD diagnostics. We, thus, demonstrate that DBN is a competitive tool for exacerbation risk assessment for patients suffering from COPD.
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What OpenQuestion holds
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.