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.
Journal ArticleDOI
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.
Journal ArticleDOI
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.
Journal ArticleDOI
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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Book ChapterDOI
Propositionalization approaches to relational data mining
TL;DR: An extension to the LINUS propositionalization method that overcomes the system's earlier inability to deal with non-determinate local variables is described, and it is shown that in many relational data mining applications this can be done without loss of predictive performance.
Reference EntryDOI
Bayesian Network Classifiers
TL;DR: The main concepts behind statistical pattern classifiers and Bayesian networks, including the main methods for the automated induction of these models are reviewed, and the advantages of Bayesian network classifiers over other types of classifiers are discussed.
Journal ArticleDOI
Environmental lead exposure during early childhood.
TL;DR: Lead-contaminated house dust is the major source of lead intake during early childhood, and black children remain at increased risk for higher blood lead concentration after adjusting for environmental lead exposures and dietary intake.
Probabilistic Relational Models
Lise Getoor,Ben Taskar +1 more
TL;DR: This chapter contains sections titled: Introduction, PRM Representation, The Difference between PRMs and Bayesian Networkss, PRMs with Structural Uncertainty, Probabilistic Model of Link Structure, PR Ms with Class Hierarchies, Inference in PRMs, Learning, Conclusion.
Journal ArticleDOI
Lead (Pb) in Tap Water and in Blood: Implications for Lead Exposure in the United States
TL;DR: Sources of lead in tap water, chemical forms of the lead, and relevant U.S. regulations/guidelines are described, while considering their implications for human exposure and some of the challenges in making such associations are highlighted.
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