Identifying risk factors of developing type 2 diabetes from an adult population with initial prediabetes using a Bayesian network

Bayesian network model of ethno-racial disparities in cardiometabolic-based chronic disease using NHANES 1999-2018

Background

Ethno-racial disparities in cardiometabolic diseases are driven by socioeconomic, behavioral, and environmental factors. Bayesian networks offer an approach to analyze the complex interaction of the multi-tiered modifiable factors and non-modifiable demographics that influence the incidence and progression of cardiometabolic disease.

Methods

In this study, we learn the structure and parameters of a Bayesian network based on 20 years of data from the US National Health and Nutrition Examination Survey to explore the pathways mediating associations between ethno-racial group and cardiometabolic outcomes. The impact of different factors on cardiometabolic outcomes by ethno-racial group is analyzed using conditional probability queries.

Results

Multiple pathways mediate the indirect association from ethno-racial group to cardiometabolic outcomes: (1) ethno-racial group to education and to behavioral factors (diet); (2) education to behavioral factors (smoking, physical activity, and-via income-to alcohol); (3) and behavioral factors to adiposity-based chronic disease (ABCD) and then other cardiometabolic drivers. Improved diet and physical activity are associated with a larger decrease in probability of ABCD stage 4 among non-Hispanic White (NHW) individuals compared to non-Hispanic Black (NHB) and Hispanic (HI) individuals.

Conclusion

Education, income, and behavioral factors mediate ethno-racial disparities in cardiometabolic outcomes, but traditional behavioral factors (diet and physical activity) are less influential among NHB or HI individuals compared to NHW individuals. This suggests the greater contribution of unmeasured individual- and/or neighborhood-level structural determinants of health that impact cardiometabolic drivers among NHB and HI individuals. Further study is needed to discover the nature of these unmeasured determinants to guide cardiometabolic care in diverse populations.

Identifying the interplay between protective measures and settings on the SARS-CoV-2 transmission using a Bayesian network

Contact tracing played a crucial role in minimizing the onward dissemination of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) in the recent pandemic. Previous studies had also shown the effectiveness of preventive measures such as mask-wearing, physical distancing, and exposure duration in reducing SARS-CoV-2 transmission. However, there is still a lack of understanding regarding the impact of various exposure settings on the spread of SARS-CoV-2 within the community, as well as the most effective preventive measures, considering the preventive measures adherence in different daily scenarios. We aimed to evaluate the effect of individual protective measures and exposure settings on the community transmission of SARS-CoV-2. Additionally, we aimed to investigate the interaction between different exposure settings and preventive measures in relation to such SARS-CoV-2 transmission. Routine SARS-CoV-2 contact tracing information was supplemented with additional data on individual measures and exposure settings collected from index patients and their close contacts. We used a case-control study design, where close contacts with a positive test for SARS-CoV-2 were classified as cases, and those with negative results classified as controls. We used the data collected from the case-control study to construct a Bayesian network (BN). BNs enable predictions for new scenarios when hypothetical information is introduced, making them particularly valuable in epidemiological studies. Our results showed that ventilation and time of exposure were the main factors for SARS-CoV-2 transmission. In long time exposure, ventilation was the most effective factor in reducing SARS-CoV-2, while masks and physical distance had on the other hand a minimal effect in this ventilation spaces. However, face masks and physical distance did reduce the risk in enclosed and unventilated spaces. Distance did not reduce the risk of infection when close contacts wore a mask. Home exposure presented a higher risk of SARS-CoV-2 transmission, and any preventive measures posed a similar risk across all exposure settings analyzed. Bayesian network analysis can assist decision-makers in refining public health campaigns, prioritizing resources for individuals at higher risk, and offering personalized guidance on specific protective measures tailored to different settings or environments.

Unraveling atherosclerotic cardiovascular disease risk factors through conditional probability analysis with Bayesian networks: insights from the AZAR cohort study

This study aimed at modelling the underlying predictor of ASCVD through the Bayesian network (BN). Data for the AZAR Cohort Study, which evaluated 500 healthcare providers in Iran, was collected through examinations, and blood samples. Two BNs were used to explore a suitable causal model for analysing the underlying predictor of ASCVD; Bayesian search through an algorithmic approach and knowledge-based BNs. Results showed significant differences in ASCVD risk factors across background variables' levels. The diagnostic indices showed better performance for the knowledge-based BN (Area under ROC curve (AUC) = 0.78, Accuracy = 76.6, Sensitivity = 62.5, Negative predictive value (NPV) = 96.0, Negative Likelihood Ratio (LR-) = 0.48) compared to Bayesian search (AUC = 0.76, Accuracy = 72.4, Sensitivity = 17.5, NPV = 93.2, LR- = 0.83). In addition, we decided on knowledge-based BN because of the interpretability of the relationships. Based on this BN, being male (conditional probability = 63.7), age over 45 (36.3), overweight (51.5), Mets (23.8), diabetes (8.3), smoking (10.6), hypertension (12.1), high T-C (28.5), high LDL-C (23.9), FBS (12.1), and TG (25.9) levels were associated with higher ASCVD risk. Low and normal HDL-C levels also had higher ASCVD risk (35.3 and 37.4), while high HDL-C levels had lower risk (27.3). In conclusion, BN demonstrated that ASCVD was significantly associated with certain risk factors including being older and overweight male, having a history of Mets, diabetes, hypertension, having high levels of T-C, LDL-C, FBS, and TG, but Low and normal HDL-C and being a smoker. The study may provide valuable insights for developing effective prevention strategies for ASCVD in Iran.

A Bayesian network perspective on neonatal pneumonia in pregnant women with diabetes mellitus

OBJECTIVE

To predict the influencing factors of neonatal pneumonia in pregnant women with diabetes mellitus using a Bayesian network model. By examining the intricate network connections between the numerous variables given by Bayesian networks (BN), this study aims to compare the prediction effect of the Bayesian network model and to analyze the influencing factors directly associated to neonatal pneumonia.

METHOD

Through the structure learning algorithms of BN, Naive Bayesian (NB), Tree Augmented Naive Bayes (TAN), and k-Dependence Bayesian Classifier (KDB), complex networks connecting variables were presented and their predictive abilities were tested. The BN model and three machine learning models computed using the R bnlean package were also compared in the data set.

RESULTS

In constraint-based algorithms, three algorithms had different presentation DAGs. KDB had a better prediction effect than NB and TAN, and it achieved higher AUC compared with TAN. Among three machine learning modes, Support Vector Machine showed a accuracy rate of 91.04% and 67.88% of precision, which was lower than TAN (92.70%; 72.10%).

CONCLUSION

KDB was applicable, and it can detect the dependencies between variables, identify more potential associations and track changes between variables and outcome.