Traditional Chinese medicine: A Bayesian network model of public awareness, usage determinants, and perception of scientific support in Austria

Introduction Despite the paucity of evidence verifying its efficacy and safety, traditional Chinese medicine (TCM) is expanding in popularity and political support. Decisions to include TCM diagnoses in the International Classification of Diseases 11th Revision (ICD-11) by the World Health Organization (WHO) and campaigns to integrate TCM into national healthcare systems have occurred whilst the public perception and usage of TCM, especially in Europe, remains undetermined. Accordingly, this study investigates the popularity, usage patterns, perception of scientific support for TCM, and its relationship to homeopathy. Methods A cross-sectional survey was performed in Austria and data from 1382 participants were analysed. A Bayesian network model retrieved partial correlations indicating distinct associations between sociodemographic determinants, complementary and alternative medicine (CAM) usage patterns, readiness to vaccinate, and TCM related variables. Results TCM was broadly known by the Austrian population (89.9% of women, 90.6% of men), with 58.9% of women and 39.5% of men using TCM between 2016 and 2019. 66.4% of women and 49.7% of men agreed with TCM being supported by science. We found a strong positive relationship between the perceived scientific support for TCM and trust in TCM-certified medical doctors. Moreover, perceived scientific support for TCM was negatively correlated with the proclivity to get vaccinated. Additionally, our Bayesian network model yielded distinct associations between TCM-, homeopathy-, and vaccination-related variables. Conclusion TCM is widely known within the Austrian general population and actively used by a substantial proportion. However, a crucial disparity exists between the commonly held public perception that TCM is scientific and findings from evidence-based studies. As public opinion towards TCM, and the proclivity to use it, are promoted through institutionalisation and official acknowledgement, it would be critical to sustain and support the distribution of unbiased, science-driven information by governmental institutions and policymakers to encourage informed patient-driven decision-making.

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A Hierarchical Bayesian Network Model for Flood Resilience Quantification of Housing Infrastructure Systems

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part A Civil Engineering ◽

10.1061/ajrua6.0001108 ◽

2021 ◽

Vol 7 (1) ◽

pp. 04020060

Author(s):

Mrinal Kanti Sen ◽

Subhrajit Dutta ◽

Jahir Iqbal Laskar

Keyword(s):

Bayesian Network ◽

Network Model ◽

Hierarchical Bayesian ◽

Bayesian Network Model ◽

Infrastructure Systems

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Predicting the risk of GenX contamination in private well water using a machine-learned Bayesian network model

Journal of Hazardous Materials ◽

10.1016/j.jhazmat.2021.125075 ◽

2021 ◽

pp. 125075

Author(s):

Javad Roostaei ◽

Sarah Colley ◽

Riley Mulhern ◽

Andrew A. May ◽

Jacqueline MacDonald Gibson

Keyword(s):

Bayesian Network ◽

Network Model ◽

Well Water ◽

Bayesian Network Model

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Impact Mechanism of the Ecological Vulnerability of Highly Developed Islands Based on the Bayesian Network Model—Applied to the Changshan Islands

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18084150 ◽

2021 ◽

Vol 18 (8) ◽

pp. 4150

Author(s):

Keyu Qin ◽

Haijun Huang ◽

Jingya Liu ◽

Liwen Yan ◽

Yanxia Liu ◽

...

Keyword(s):

Bayesian Network ◽

Network Model ◽

Geographical Information ◽

Global Changes ◽

Bayesian Network Model ◽

Island Ecosystems ◽

Impact Mechanism ◽

The North ◽

Ecological Vulnerability ◽

The Impact

Islands are one of the most sensitive interfaces between global changes and land and sea dynamic effects, with high sensitivity and low stability. Therefore, under the dynamic coupling effect of human activities and frequent natural disasters, the vulnerability of the ecological environment of islands shows the characteristics of complexity and diversity. For the protection of island ecosystems, a system for the assessment of island ecosystems and studies on the mechanism of island ecological vulnerability are highly crucial. In this study, the North and South Changshan Islands of China were selected as the study area. Considering various impact factors of island ecological vulnerability, the geographical information systems (GIS) spatial analysis, field surveys, data sampling were used to evaluate island ecological vulnerability. The Bayesian network model was used to explore the impact mechanism of ecological vulnerability. The results showed that the ecological vulnerability of the North Changshan Island is higher than that of the South Changshan Island. Among all the indicators, the proportion of net primary productivity (NPP) and the steep slope has the strongest correlation with ecological vulnerability. This study can be used as references in the relevant departments to formulate management policies and promote the sustainable development of islands and their surrounding waters

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Misper-Bayes: A Bayesian Network Model for Missing Person Investigations

IEEE Access ◽

10.1109/access.2021.3069081 ◽

2021 ◽

pp. 1-1

Author(s):

Denis Reilly ◽

Mark Taylor ◽

Paul Fergus ◽

Carl Chalmers ◽

Steven Thompson

Keyword(s):

Bayesian Network ◽

Network Model ◽

Bayesian Network Model

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A Causal Bayesian Network Model for Resolving Complex Wicked Problems

2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) ◽

10.1109/iemtronics52119.2021.9422608 ◽

2021 ◽

Author(s):

Daniel T. Semwayo ◽

Ritesh Ajoodha

Keyword(s):

Bayesian Network ◽

Network Model ◽

Wicked Problems ◽

Bayesian Network Model ◽

Causal Bayesian Network

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Probabilistic Model-Based Fault Diagnosis of the Rotor System

ASME 2007 Power Conference ◽

10.1115/power2007-22072 ◽

2007 ◽

Author(s):

Jiye Shao ◽

Rixin Wang ◽

Jingbo Gao ◽

Minqiang Xu

Keyword(s):

Fault Diagnosis ◽

Bayesian Network ◽

Network Model ◽

Rotating Machinery ◽

Rotor System ◽

Bayesian Network Model ◽

Diagnosis System ◽

Model Based ◽

Core Components ◽

Fault Diagnosis System

The rotor is one of the most core components of the rotating machinery and its working states directly influence the working states of the whole rotating machinery. There exists much uncertainty in the field of fault diagnosis in the rotor system. This paper analyses the familiar faults of the rotor system and the corresponding faulty symptoms, then establishes the rotor’s Bayesian network model based on above information. A fault diagnosis system based on the Bayesian network model is developed. Using this model, the conditional probability of the fault happening is computed when the observation of the rotor is presented. Thus, the fault reason can be determined by these probabilities. The diagnosis system developed is used to diagnose the actual three faults of the rotor of the rotating machinery and the results prove the efficiency of the method proposed.

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