Selection of water treatment processes using Bayesian decision network analyses

2007 ◽  
Vol 6 (1) ◽  
pp. 95-102 ◽  
Author(s):  
Zoe J.Y. Zhu ◽  
Edward A McBean
Keyword(s):  
Water Treatment ◽  
Bayesian Decision ◽  
Network Analyses ◽  
Treatment Processes ◽  
Decision Network ◽  
Bayesian Decision Network ◽  
Selection Of

scholarly journals Use of artificial intelligence based models for learning better policy for maternal and child health

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
T Sethi ◽  
R Awasthi
Keyword(s):  
Artificial Intelligence ◽  
Adverse Pregnancy Outcome ◽  
Bayesian Decision ◽  
Learning From Data ◽  
Decision Network ◽  
Sanitation And Hygiene ◽  
Public Health Challenges ◽  
Bayesian Decision Network ◽  
Adverse Pregnancy ◽  
Bayesian Artificial Intelligence

Abstract More than 640,000 babies died of sepsis before they reach the age of one month in India in 2016. Despite a large number of government schemes aimed at reducing this rate, this number still remains high because of the complexity and interplay of factors involved. Finding an optimum policy and solutions to this problem needs learning from data. We integrated diverse sources of data and applied Bayesian Artificial Intelligence methods for learning to mitigate sepsis and adverse pregnancy outcomes in India. In this project, we created models that combine the robustness of ensemble averaged Baeysian Networks with decision learning and impact evaluation by using simulations and counterfactual reasoning respectively. We will demonstrate the process of learning these models and how these led us to infer the pivotal role of Water, Sanitation and Hygiene for reducing Adverse Pregnancy Outcome and neonatal sepsis in the population studied. We will also demonstrate the creation of explainable AI models for complex public health challenges and their deployment with wiseR, our in-house, open source platform for doing end-to-end Bayesian Decision Network learning.

Evaluation of a Bayesian decision network for diagnosing pyloric stenosis

2006 ◽  
Vol 41 (1) ◽  
pp. 155-161 ◽  
Author(s):  
Sonia M. Alvarez ◽  
Beverly A. Poelstra ◽  
Randall S. Burd
Keyword(s):  
Pyloric Stenosis ◽  
Bayesian Decision ◽  
Decision Network ◽  
Bayesian Decision Network

scholarly journals Learning to Address Health Inequality in the United States with a Bayesian Decision Network

2019 ◽  
Vol 33 ◽  
pp. 710-717
Author(s):  
Tavpritesh Sethi ◽  
Anant Mittal ◽  
Shubham Maheshwari ◽  
Samarth Chugh
Keyword(s):  
United States ◽  
Web Application ◽  
The United States ◽  
Care Quality ◽  
Joint Probability Distribution ◽  
Bayesian Decision ◽  
Unified Framework ◽  
Decision Network ◽  
Bayesian Decision Network ◽  
The Impact

Life-expectancy is a complex outcome driven by genetic, socio-demographic, environmental and geographic factors. Increasing socio-economic and health disparities in the United States are propagating the longevity-gap, making it a cause for concern. Earlier studies have probed individual factors but an integrated picture to reveal quantifiable actions has been missing. There is a growing concern about a further widening of healthcare inequality caused by Artificial Intelligence (AI) due to differential access to AI-driven services. Hence, it is imperative to explore and exploit the potential of AI for illuminating biases and enabling transparent policy decisions for positive social and health impact. In this work, we reveal actionable interventions for decreasing the longevitygap in the United States by analyzing a County-level data resource containing healthcare, socio-economic, behavioral, education and demographic features. We learn an ensembleaveraged structure, draw inferences using the joint probability distribution and extend it to a Bayesian Decision Network for identifying policy actions. We draw quantitative estimates for the impact of diversity, preventive-care quality and stablefamilies within the unified framework of our decision network. Finally, we make this analysis and dashboard available as an interactive web-application for enabling users and policy-makers to validate our reported findings and to explore the impact of ones beyond reported in this work.

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