國立虎尾科技大學 |

Advancing Environmental Human Health Risk Assessment through Bayesian Network Analysis.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Advancing Environmental Human Health Risk Assessment through Bayesian Network Analysis./
作者:	Zabinski, Joseph W.
面頁冊數:	1 online resource (172 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
Contained By:	Dissertation Abstracts International79-01B(E).
標題:	Environmental engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780355179064

Advancing Environmental Human Health Risk Assessment through Bayesian Network Analysis.
Zabinski, Joseph W.

Advancing Environmental Human Health Risk Assessment through Bayesian Network Analysis. - 1 online resource (172 pages)

Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

Regulatory agencies rely on quantitative risk assessment to design policies, such as environmental quality standards, to protect public health. Although risk assessment forms the foundation of important policy decisions, recent reviews have indicated the need for technical and practical improvements to risk assessment. This dissertation advances the application of Bayesian networks (BNs) in environmental human health risk assessment in response to this need. BNs were developed to support causal inference in artificial intelligence applications but are not currently used by environmental regulatory agencies.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355179064Subjects--Topical Terms:

557376
Environmental engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Advancing Environmental Human Health Risk Assessment through Bayesian Network Analysis.
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245 1 0 $a Advancing Environmental Human Health Risk Assessment through Bayesian Network Analysis.
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300 $a 1 online resource (172 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
500 $a Adviser: Jacqueline MacDonald Gibson.
502 $a Thesis (Ph.D.) $c The University of North Carolina at Chapel Hill $d 2017.
504 $a Includes bibliographical references
520 $a Regulatory agencies rely on quantitative risk assessment to design policies, such as environmental quality standards, to protect public health. Although risk assessment forms the foundation of important policy decisions, recent reviews have indicated the need for technical and practical improvements to risk assessment. This dissertation advances the application of Bayesian networks (BNs) in environmental human health risk assessment in response to this need. BNs were developed to support causal inference in artificial intelligence applications but are not currently used by environmental regulatory agencies.
520 $a First, a proof-of-concept BN is developed to test BN performance in predicting the effect of maternal exposure to arsenic in drinking water on the risk of newborn lower birthweight for gestational age. The network is the first of its kind to model a dose-response relationship connecting an environmental hazard to a human health outcome. In addition, unlike prevailing regulatory risk assessment approaches, it accounts for inter-individual metabolic differences. The BN is shown to outperform current regulatory risk assessment methods in balancing predictive sensitivity and specificity.
520 $a Second, a BN is developed to predict the effect of arsenic exposure in drinking water on the risk of diabetes and prediabetes, while accounting for inter-individual differences in arsenic metabolism and body mass index. In addition, the BN's utility to risk managers is demonstrated by using the model to predict the population-level health consequences of reduced arsenic exposure (including decreased diabetes prevalence). These predictions demonstrate the importance of considering both cancer and non-cancer outcomes when making policy. BNs' ability to facilitate cost-benefit calculations in regulatory contexts is highlighted.
520 $a Finally, improvements to risk assessment utility by using BNs are illustrated through a model developed to quantify risk to wastewater treatment workers of contracting Ebola virus disease from contact with contaminated wastewater during an outbreak. The model is used to identify key factors affecting risk and captures risk under different mitigation strategies.
520 $a These results suggest that BNs offer a quantitatively sophisticated, flexible, and transparent method that addresses key challenges in current risk assessment practice in support of policymaking.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Environmental engineering. $3 557376
650 4 $a Statistics. $3 556824
650 4 $a Public health. $3 560998
655 7 $a Electronic books. $2 local $3 554714
690 $a 0775
690 $a 0463
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710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The University of North Carolina at Chapel Hill. $b Environmental Sciences and Engineering. $3 1181780
773 0 $t Dissertation Abstracts International $g 79-01B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10286574 $z click for full text (PQDT)