ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2023
Fractional Dynamics Foster Deep Learning of COPD Stage Prediction.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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.
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.
Who cites it
14 citing papers in PubMed, 31 citations in OpenAlex.
- Copper-Driven Epithelial Barrier Disruption: A Novel Mechanism of COPD Acute Exacerbations Mediated by the TNF-α/ATP7A Axis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Long-range interdependencies aware signal analysis for rapid and accurate obstructive sleep apnea prediction.Nature communications · 2026Article
- Structural determinants of pulmonary diffusing capacity identified by network analysis and machine learning on quantitative CT.Scientific reports · 2026Article
- Clinically aligned COPD severity prediction using ordinal neural networks.Frontiers in medicine · 2026Article
- Prediction of chronic obstructive pulmonary disease based on multimodal data and deep learning.APL bioengineering · 2025Article
- ADAR1-HNRNPL-Mediated CircCANX Decline Promotes Autophagy in Chronic Obstructive Pulmonary Disease.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Deep learning for detecting and early predicting chronic obstructive pulmonary disease from spirogram time series.NPJ systems biology and applications · 2025Article
- Artificial Intelligence in Chronic Obstructive Pulmonary Disease: Research Status, Trends, and Future Directions --A Bibliometric Analysis from 2009 to 2023.International journal of chronic obstructive pulmonary disease · 2024Article
- Externally validated deep learning model to identify prodromal Parkinson's disease from electrocardiogram.Scientific reports · 2023Article
- Molecule generation using transformers and policy gradient reinforcement learning.Scientific reports · 2023Article
- Supervised deep learning with vision transformer predicts delirium using limited lead EEG.Scientific reports · 2023Article
- Machine Learning Assisted Wearable Wireless Device for Sleep Apnea Syndrome Diagnosis.Biosensors · 2023Article
- Fractional Dynamics Foster Deep Learning of COPD Stage Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- A Macrophage-Related Gene Signature for Identifying COPD Based on Bioinformatics and ex vivo Experiments.Journal of inflammation research · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
Abstract
Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death worldwide. Current COPD diagnosis (i.e., spirometry) could be unreliable because the test depends on an adequate effort from the tester and testee. Moreover, the early diagnosis of COPD is challenging. The authors address COPD detection by constructing two novel physiological signals datasets (4432 records from 54 patients in the WestRo COPD dataset and 13824 medical records from 534 patients in the WestRo Porti COPD dataset). The authors demonstrate their complex coupled fractal dynamical characteristics and perform a fractional-order dynamics deep learning analysis to diagnose COPD. The authors found that the fractional-order dynamical modeling can extract distinguishing signatures from the physiological signals across patients with all COPD stages-from stage 0 (healthy) to stage 4 (very severe). They use the fractional signatures to develop and train a deep neural network that predicts COPD stages based on the input features (such as thorax breathing effort, respiratory rate, or oxygen saturation). The authors show that the fractional dynamic deep learning model (FDDLM) achieves a COPD prediction accuracy of 98.66% and can serve as a robust alternative to spirometry. The FDDLM also has high accuracy when validated on a dataset with different physiological signals.
Indexed as
Identifiers
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.