Risk Assessment for Primary Coronary Heart Disease Event Using Dynamic Bayesian Networks

Orphanou, Kalia; Stassopoulou, Athena; Keravnou, Elpida

doi:10.1007/978-3-319-19551-3_20

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…28 Over the past few years, BNs have been extensively used to model clinical problems in CVD for the purposes of diagnosis, risk assessment and disease prediction. [29][30][31][32][33] In the present study, we introduced a BN analysis to evaulate the aetiological role of HCV infection in CVD risk. Our objective is to characterise the multivariable probabilistic connection between the two diseases and identify factors that mediate and influence this relationship in a population of Canadian adults.…”

Section: Open Accessmentioning

confidence: 99%

Bayesian network modelling study to identify factors influencing the risk of cardiovascular disease in Canadian adults with hepatitis C virus infection

Badawi

Giuseppe

Gupta³

et al. 2020

BMJ Open

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ObjectivesThe present study evaluates the extent of association between hepatitis C virus (HCV) infection and cardiovascular disease (CVD) risk and identifies factors mediating this relationship using Bayesian network (BN) analysis.Design and settingA population-based cross-sectional survey in Canada.ParticipantsAdults from the Canadian Health Measures Survey (n=10 115) aged 30 to 74 years.Primary and secondary outcome measuresThe 10-year risk of CVD was determined using the Framingham Risk Score in HCV-positive and HCV-negative subjects. Using BN analysis, variables were modelled to calculate the probability of CVD risk in HCV infection.ResultsWhen the BN is compiled, and no variable has been instantiated, 73%, 17% and 11% of the subjects had low, moderate and high 10-year CVD risk, respectively. The conditional probability of high CVD risk increased to 13.9%±1.6% (p<2.2×10-16) when the HCV variable is instantiated to ‘Present’ state and decreased to 8.6%±0.2% when HCV was instantiated to ‘Absent’ (p<2.2×10-16). HCV cases had 1.6-fold higher prevalence of high-CVD risk compared with non-infected individuals (p=0.038). Analysis of the effect modification of the HCV-CVD relationship (using median Kullback-Leibler divergence; DKL) showed diabetes as a major effect modifier on the joint probability distribution of HCV infection and CVD risk (DKL=0.27, IQR: 0.26 to 0.27), followed by hypertension (0.24, IQR: 0.23 to 0.25), age (0.21, IQR: 0.10 to 0.38) and injection drug use (0.19, IQR: 0.06 to 0.59).ConclusionsExploring the relationship between HCV infection and CVD risk using BN modelling analysis revealed that the infection is associated with elevated CVD risk. A number of risk modifiers were identified to play a role in this relationship. Targeting these factors during the course of infection to reduce CVD risk should be studied further.

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Section: Open Accessmentioning

confidence: 99%

Bayesian network modelling study to identify factors influencing the risk of cardiovascular disease in Canadian adults with hepatitis C virus infection

Badawi

Giuseppe

Gupta³

et al. 2020

BMJ Open

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“…Coronary heart disease (CHD), a leading cause of mortality worldwide ( 1 ), often develops as a result of myocardial ischemia/hypoxia, secondary to coronary atherosclerosis (CAS)-induced stenosis ( 2 ). In 2009, the mortality of CHD patients in China was ~94.9/100,000 in cities, which was higher than that in the rural areas (71.3/100,000) ( 3 ).…”

Section: Introductionmentioning

confidence: 99%

Astragalosides promote angiogenesis via vascular endothelial growth factor and basic fibroblast growth factor in a rat model of myocardial infarction

Zhang

Jiang

et al. 2015

Molecular Medicine Reports

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The aim of the present study was to evaluate the effect of astragalosides (ASTs) on angiogenesis, as well as the expression of vascular endothelial growth factor (VEGF) and basic fibroblast growth factor (bFGF) following myocardial infarction (MI). MI was induced in rats by ligation of the left coronary artery. Twenty-four hours after surgery, the rats were divided into low-dose, high-dose, control and sham surgery groups (n=8 per group). The low- and high-dose groups were treated with ASTs (2.5 and 10 mg/kg/day, respectively, via intraperitoneal injection), while, the control and sham surgery group rats received saline. Serum levels, and mRNA and protein expression levels of VEGF and bFGF, as well as the microvessel density (MVD) were determined four weeks post-treatment. Twenty-four hours post-surgery, VEGF and bFGF serum levels were observed to be comparable between the groups; while at four weeks, the VEGF and bFGF levels were higher in the AST-treated rats (P<0.01). Similarly, VEGF and bFGF mRNA and protein expression levels were higher following AST treatment (P<0.05). No difference in VEGF mRNA expression between the low- and high-dose groups was noted, however, an increase in the bFGF expression levels was detected in the high-dose group. Newly generated blood vessels were observed following MI, with a significant increase in MVD observed in the AST-treated groups (P<0.05). AST promotes angiogenesis of the heart and increases VEGF and bFGF expression levels. Thus, it is hypothesized that increased VEGF and bFGF levels may contribute to the AST-induced increase in angiogenesis in rat models of MI.

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“…However the Bayesian Networks has high computational overhead. Integration of Dynamic Bayesian Network (DBN) and Temporal Abstractions (TAs) [11] presented for assessing the intricacy of coronary heart disease (CHD) event. The conventional TAs and DBNs were analyzed to enhance the model to a longitudinal CHD dataset.…”

Section: Literature Surveymentioning

confidence: 99%

A fuzzy analytic hierarchy attribute weighting and deep learning for improving CHD prediction of optimized semi parametric extended dynamic bayesian network

Gomathi¹,

Priyaa²

2017

IJET

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Several Data mining techniques have been developed to enhance the prediction accuracy and analyze several events in Coronary Heart Disease (CHD). One among them was Extended Dynamic Bayesian Network (EDBN) which integrates temporal abstractions with DBN. Then EDBN was extended as Optimized Semi parametric Extended Dynamic Bayesian Network (OSEDBN) to handle Complex temporal abstractions in irregular interval time series data. The deep learning network is generated the various time points in the next level to improve the analysis and prediction of CHD. In this paper, Optimized Semi parametric Extended Deep Dynamic Bayesian Network (OSEDDBN) is proposed by integrating deep learning architecture with OSEDBN to improve the ability of extracting more important data and support complex structures from various types of input sources. Additionally the Fuzzy Analytic Hierarchy Process (FAHP) approach is used to compute the global weights for the attributes based on their individual contribution. The global weights of the attributes obtained by FAHP are utilized for training OSEDDBN to further improve the prediction of Coronary Heart Disease (CHD) risks. The performance of EDBN, OSEDBN, OSEDDBN, and OSEDDBN-FAHP are evaluated in terms of Precision, Recall and FMeasure.

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Risk Assessment for Primary Coronary Heart Disease Event Using Dynamic Bayesian Networks

Cited by 4 publications

References 6 publications

Bayesian network modelling study to identify factors influencing the risk of cardiovascular disease in Canadian adults with hepatitis C virus infection

Bayesian network modelling study to identify factors influencing the risk of cardiovascular disease in Canadian adults with hepatitis C virus infection

Astragalosides promote angiogenesis via vascular endothelial growth factor and basic fibroblast growth factor in a rat model of myocardial infarction

A fuzzy analytic hierarchy attribute weighting and deep learning for improving CHD prediction of optimized semi parametric extended dynamic bayesian network

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