DocumentCode
2367428
Title
BBN-Based Decision Support for Health Risk Analysis
Author
Liu, Kevin F R ; Lu, Che-Fan
Author_Institution
Dept. of Safety, Ming Chi Univ. of Technol., Taipei, Taiwan
fYear
2009
fDate
25-27 Aug. 2009
Firstpage
696
Lastpage
702
Abstract
Human health risk assessment (HRA) is the process to estimate the nature and probability of adverse health effects in humans. There are three issues about HRA: (1) hazard index (HI) cannot detail related diseases; moreover, we cannot exactly recognize the meaning of HI levels; (2) the same problems will also occur in human cancer risk; (3) the assumption of additive effect in human health for more than two toxic chemicals is problematic due to the violation of toxicology. To address these issues, this study proposes to use Bayesian belief networks (BBN) as a decision support for the higher level risk estimate which is able to represent the probabilistic relationships between all kinds of health effects and air pollutants. The study has several objectives: (1) to design the health risk procedure for BBN approach; (2) to represent the relationships between toxic substances, diseases and human effects with BBN; (3) to construct dose-response relationships by BBN; (4) to predict human disease and cancer risks due to specific toxic substance by BBN; and (5) case studies.
Keywords
Bayes methods; air pollution; belief networks; diseases; health care; health hazards; risk analysis; toxicology; BBN-based decision support; Bayesian belief network; adverse health effect; air pollutant; disease; hazard index; health risk analysis; health risk assessment; human cancer risk; human health; probabilistic relationship; toxic chemical; toxic substance; toxicology; Air pollution; Bayesian methods; Cancer; Diseases; Hazards; Humans; Risk analysis; Risk management; Toxic chemicals; Toxicology; Bayesian Networks; Health Risk Analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
INC, IMS and IDC, 2009. NCM '09. Fifth International Joint Conference on
Conference_Location
Seoul
Print_ISBN
978-1-4244-5209-5
Electronic_ISBN
978-0-7695-3769-6
Type
conf
DOI
10.1109/NCM.2009.187
Filename
5331805
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