A new approach to a legacy concern: Evaluating machine-learned Bayesian networks to predict childhood lead exposure risk from community water systems.
Riley Mulhern,Javad Roostaei,Sara Schwetschenau,Tejas Pruthi,Chris Campbell,Jacqueline MacDonald Gibson +5 more
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In this paper, the relationship between children's blood lead levels and drinking water system characteristics using machine-learned Bayesian networks was assessed using blood lead records from 2003 to 2017 for 40,742 children in Wake County, North Carolina.About:
This article is published in Environmental Research.The article was published on 2022-03-01 and is currently open access. It has received 4 citations till now. The article focuses on the topics: Lead (geology) & Risk assessment.read more
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Improved Decision Making for Water Lead Testing in U.S. Child Care Facilities Using Machine-Learned Bayesian Networks
TL;DR: In this paper , machine-learned Bayesian network (BN) models were used to predict building-wide water lead risk in over 4,000 child care facilities in North Carolina according to maximum and 90th percentile lead levels from water lead concentrations at 22,943 taps.
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Differential exposure to drinking water contaminants in North Carolina: Evidence from structural topic modeling and water quality data.
TL;DR: In this paper , structural topic modeling (STM) and geographic mapping is used to identify the main topics and pollutant categories being researched and the areas exposed to drinking water contaminants.
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Predictive modeling of indoor dust lead concentrations: Sources, risks, and benefits of intervention.
Matthew Dietrich,Cynthia F Barlow,Jane Entwistle,Diana Meza-Figueroa,Chenyin Dong,Peggy Gunkel-Grillon,Khadija Jabeen,Lindsay Bramwell,John T. Shukle,L. Wood,Ravi Naidu,Kara L. Fry,Mark Taylor,Gabriel M. Filippelli +13 more
TL;DR: In this article , a global dataset (∼40 countries, n = 1951) of community sourced household dust samples were used to predict whether indoor dust was elevated in Pb, expanding on recent work in the United States.
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Intelligent lung cancer MRI prediction analysis based on cluster prominence and posterior probabilities utilizing intelligent Bayesian methods on extracted gray-level co-occurrence (GLCM) features
Jing Yang,Por Lip Yee,Hanen Karamti,Elsayed Mohamed Tag Eldin,Amjad Aldweesh,Atef El Jery,Lal Hussain,Abdulfattah Omar +7 more
TL;DR: In this article, a detailed posterior probabilities analysis was conducted to unfold the network associations among the gray-level co-occurrence matrix (GLCM) features, and the cluster prominence was selected as target node.
References
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Advancing Dose-Response Assessment Methods for Environmental Regulatory Impact Analysis: A Bayesian Belief Network Approach Applied to Inorganic Arsenic.
Joseph W. Zabinski,Gonzalo G. García-Vargas,Marisela Rubio-Andrade,Rebecca C. Fry,Jacqueline MacDonald Gibson +4 more
TL;DR: A BBN-based model predicts birthweight at gestational age from arsenic exposure via drinking water and maternal metabolic indicators using a cohort of 200 pregnant women from an arsenic-endemic region of Mexico, and outperforms prevailing approaches in balancing false-positive and false-negative rates.
Journal ArticleDOI
Water Services Management and Governance
TL;DR: In this article, the complexity of the water services sector is analyzed based on a historical analysis of developments within the sector. And the authors argue that an understanding of the past is a necessity to explore potential, probable and preferable future.
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