ArticleScientific reports2022
Exploring influencing factors of chronic obstructive pulmonary disease based on elastic net and Bayesian network.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 21 citations in OpenAlex.
- Exploring stroke risk factors in different genders using Bayesian networks: a cross-sectional study involving a population of 134,382.BMC public health · 2025Article
- The interaction network and potential clinical effectiveness of dimensional psychopathology phenotyping based on EMR: a Bayesian network approach.BMC psychiatry · 2025Article
- Reporting Standards for Bayesian Network Modelling.Entropy (Basel, Switzerland) · 2025Article
- Assessment and analysis of factors influencing suicidal ideation in young adults: a large cohort study using an elastic network logistic regression model.BMC psychiatry · 2025Article
- Application of Elastic networks and Bayesian networks to explore influencing factors associated with arthritis in middle-aged and older adults in the Chinese community.Frontiers in public health · 2025Article
- The identification and prediction of atrial fibrillation in coronary artery disease patients: a multicentre retrospective study based on Bayesian network.Annals of medicine · 2024Article
- Blood-based Transcriptomic and Proteomic Biomarkers of Emphysema.American journal of respiratory and critical care medicine · 2024Article
- Exploring factors related to heart attack complicated with hypertension using a Bayesian network model: a study based on the China Health and Retirement Longitudinal Study.Frontiers in public health · 2023Article
- Using Bayesian networks with Max-Min Hill-Climbing algorithm to detect factors related to multimorbidity.Frontiers in cardiovascular medicine · 2022Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to construct Bayesian networks (BNs) to analyze the network relationships between COPD and its influencing factors, and the strength of each factor's influence on COPD was reflected through network reasoning. Elastic Net and Max-Min Hill-Climbing (MMHC) algorithm were adopted to screen the variables on the surveillance data of COPD among residents in Shanxi Province, China from 2014 to 2015, and construct BNs respectively. 10 variables finally entered the model after screening by Elastic Net. The BNs constructed by MMHC showed that smoking status, household air pollution, family history, cough, air hunger or dyspnea were directly related to COPD, and Gender was indirectly linked to COPD through smoking status. Moreover, smoking status, household air pollution and family history were the parent nodes of COPD, and cough, air hunger or dyspnea represented the child nodes of COPD. In other words, smoking status, household air pollution and family history were related to the occurrence of COPD, and COPD would make patients' cough, air hunger or dyspnea worse. Generally speaking, BNs could reveal the complex network linkages between COPD and its relevant factors well, making it more convenient to carry out targeted prevention and control of COPD.
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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.