Risk assessments of chronic obstructive pulmonary disease using Bayesian network based on a provincial survey

Shangguan, Changyue; Yu, Lei; Liu, Guangcong; Song, Yibing; Chen, Jie

doi:10.20452/pamw.15867

Cited by 4 publications

(3 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using GeNIe software and Eqn (6), the error rate of the BN of the selected verification set is 10%. Shangguan et al (2021) proposed that a BN model exhibits good correctness and accuracy if the error rate is lower than 15%. Thus, the constructed BNbased model can accurately predict the scenario evolution of a subway station fire.…”

Section: Model Verificationmentioning

confidence: 99%

Risk Assessment of Subway Station Fire by Using a Bayesian Network-Based Scenario Evolution Model

Li,

Yuan,

Zhang

et al. 2024

Journal of Civil Engineering and Management

View full text Add to dashboard Cite

Subway station fires frequently result in massive casualties, economic losses and even social panic due to the massive passenger flow, semiconfined space and limited conditions for escape and smoke emissions. The combination of different states of fire hazard factors increases the uncertainty and complexity of the evolution path of subway station fires and causes difficulty in assessing fire risk. Traditional methods cannot describe the development process of subway station fires, and thus, cannot assess fire risk under different fire scenarios. To realise scenario-based fire risk assessment, the elements that correspond to each scenario state during fire development in subway stations are identified in this study to explore the intrinsic driving force of fire evolution. Accordingly, a fire scenario evolution model of subway stations is constructed. Then, a Bayesian network is adopted to construct a scenario evolution probability calculation model for calculating the occurrence probability of each scenario state during subway station fire development and identifying critical scenario elements that promote fire evolution. Xi’an subway station system is used as a case to illustrate the application of Bayesian network-based scenario evolution model, providing a practical management tool for fire safety managers. The method adopted in this study enables managers to predict fire risk in each scenario and understand the evolution path of subway station fire, supporting the establishment of fire response strategies based on “scenario–response” planning.

show abstract

Section: Model Verificationmentioning

confidence: 99%

Risk Assessment of Subway Station Fire by Using a Bayesian Network-Based Scenario Evolution Model

Li,

Yuan,

Zhang

et al. 2024

Journal of Civil Engineering and Management

View full text Add to dashboard Cite

show abstract

“…Bayesian networks (BNs) are graphical models that describe the probability relationship between a set of variables ( 29 ). BNs have been increasingly used to predict the risk of particular diseases, such as accurate kidney injury ( 28 ) and chronic obstructive pulmonary disease ( 30 ), while none was found in the field of MMT yet.…”

Section: Introductionmentioning

confidence: 99%

The risk assessment of relapse among newly enrolled participants in methadone maintenance treatment: A group-LASSO based Bayesian network study

Tang

Fan

Wang

et al. 2023

Front. Public Health

View full text Add to dashboard Cite

BackgroundRelapse is a great barrier to improving the effectiveness of methadone maintenance treatment (MMT). Participants with different treatment durations could vary in their compliance with MMT, which may lead to different levels of relapse risk. This study aims to identify the risk factors for relapse and assess the relapse risk of MMT participants of different treatment durations.MethodThis retrospective study used data collected from seven MMT clinics in Guangdong Province, China, from January 2010 to April 2017. Newly enrolled participants who received 6 (n = 903) and 12 (n = 710) months of consecutive treatment with complete data were included. We selected significant risk factors for relapse through the group lasso regression and then incorporated them into Bayesian networks to reveal relationships between factors and predict the relapse risk.ResultsThe results showed that participants who received 6-month treatment had a lower relapse rate (32.0%) than those of 12-month treatment (39.0%, P < 0.05). Factors including personal living status and daily methadone dose were only influential to those who received the 6-month treatment. However, age, age at the initial drug use, HIV infection status, sexual behaviors, and continuous treatment days were common factors of both durations. The highest relapse risk for those after the 6-month treatment was inferred as 66.7% while that of the 12-month treatment was 83.3%. Farmers and those who have high accessibility to MMT services may require additional attention.ConclusionIt is necessary to implement targeted interventions and education based on the treatment durations of participants to decrease the relapse rate. Meanwhile, those about HIV/sexually transmitted infection prevention and anti-narcotics should be held in the whole process.

show abstract

“…Accumulating evidence links ambient air pollution to an increased prevalence of COPD and a more severe disease course [6][7][8][9][10]. In the last 30 years, the number of COPD deaths worldwide attributable to air pollution with particulate matter (PM) has nearly doubled [11].…”

Section: Introductionmentioning

confidence: 99%

Air pollution and long-term risk of hospital admission due to chronic obstructive pulmonary disease exacerbations in Poland: a time-stratified, case-crossover study

Dąbrowiecki¹,

Chciałowski²,

Dąbrowiecka³

et al. 2023

Polish Archives of Internal Medicine

View full text Add to dashboard Cite

What's new?Growing evidence from studies across the world has attributed substantial disease burden to air pollution. Gaseous and particulate air pollution in Poland is an important public health problem, but the disease burden related to air pollution is largely unknown. In Poland, there is a lack of long-term, multi-city studies linking air pollution to objective healthcare measures.People with chronic lung diseases, such as chronic obstructive pulmonary disease (COPD), are most susceptible to ambient air pollution. In this study, we analyzed data from Warsaw, Cracow, and the Tricity for the period from 2011 to 2018 to see how changes in the concentrations of various air pollutants were associated with changes in the number of hospitalizations due to COPD exacerbations. We found that exposure to both particulate and gaseous pollutants significantly increased the hospitalization risk, with a substantial proportion of hospitalizations attributable to air pollution.

show abstract

Risk assessments of chronic obstructive pulmonary disease using Bayesian network based on a provincial survey

Cited by 4 publications

References 33 publications

Risk Assessment of Subway Station Fire by Using a Bayesian Network-Based Scenario Evolution Model

Risk Assessment of Subway Station Fire by Using a Bayesian Network-Based Scenario Evolution Model

The risk assessment of relapse among newly enrolled participants in methadone maintenance treatment: A group-LASSO based Bayesian network study

Air pollution and long-term risk of hospital admission due to chronic obstructive pulmonary disease exacerbations in Poland: a time-stratified, case-crossover study

Contact Info

Product

Resources

About