Risk assessment of human neural tube defects using a Bayesian belief network

2009 ◽  
Vol 24 (1) ◽  
pp. 93-100 ◽  
Author(s):  
Yilan Liao ◽  
Jinfeng Wang ◽  
Yaoqin Guo ◽  
Xiaoying Zheng
Author(s):  
Gokcen Ogutcu

This study focuses on identification of risk factors in pipeline system and also, concentrates on identification of relationship between parameters. In order to achieve this purpose, Bayesian Belief Network with historical data was used to provide a framework for assessing risk relative to the company’s petroleum pipeline system. Each of the variables in the Bayesian Belief Network is described by nodes and each node has a state. Relationships between parameters are presented by arrows. Probability of any node being in state was shown in conditional probability tables. Historical data were helpful to build conditional probability tables. Variables were defined as corrosion, third party damage, mechanical and operational failure.


1998 ◽  
Vol 44 (9) ◽  
pp. 1886-1891 ◽  
Author(s):  
Anthony J A Wright ◽  
Paul M Finglas ◽  
Susan Southon

Abstract Neural tube defects can be prevented by adequate intake of periconceptional folate, and inverse associations between folate status and cardiovascular disease and various cancers have been noted. Thus, there is renewed interest in the analysis of red cell folate (RCF) as an indicator of folate deficiency risk. Assessment of the assumptions that underpin RCF assays indicates that many are false. Published literature suggests that increased deoxy-hemoglobin (which can bind RCF electrostatically) yields more assayable folate, and increased oxy-hemoglobin (which cannot bind RCF) yields less assayable folate. It is argued that as deoxy-hemoglobin picks up oxygen and switches quaternary structure, any bound folate must, on purely theoretical grounds, become physically “trapped”. Venous blood taken for analysis is 65% to 75% saturated with oxygen, and pro-rata “trapping” will lead to serious underestimation of RCF. Hence, doubt is cast over the validity of all previous RCF values. Some strategies for accurately assessing RCF are suggested.


2020 ◽  
Vol 26 (7) ◽  
pp. 614-634
Author(s):  
Li Guan ◽  
Qiang Liu ◽  
Alireza Abbasi ◽  
Michael J. Ryan

Reliable and efficient risk assessments are essential to deal effectively with potential risks in international construction projects. However, most conventional risk modeling methods are based on the hypothesis that risk factors are independent, which does not account adequately for the causal relationships among risk factors. In this study, a risk assessment model for international construction projects was developed to improve the efficacy of risk management by integrating fault tree analysis and fuzzy set theory with a Bayesian belief network. The risk rating of each risk factor, expressed as the product of risk occurrence probability and impact, was incorporated into the risk assessment model to evaluate degrees of risk. Therefore, risk factors were categorized into different risk levels taking into account their inherent causal relationships, which allowed the identification of critical risk factors. The applicability of the fuzzy Bayesian belief network-based risk assessment model was verified using a case study through a comparative analysis with the results from a fuzzy synthetic evaluation method. The comparison shows that the proposed risk assessment model is able to provide guidelines for an effective risk management process and ultimately to increase project performance in a complex environment such as international construction projects.


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