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

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 longevity-gap in the United States by analyzing a County-level data resource containing healthcare, socio-economic, behavioral, education and demographic features. We learn an ensemble-averaged 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 stable-families 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.Comment: 8 pages, 4 figures, 1 table (excluding the supplementary material), accepted for publication in AAAI 201

Management of a high mix production system with interdependent demands : simulation of proposed policies

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 92-94).The finished goods inventory management in the accessories area of a material testing company is complex. There is interdependence between the demands of products and they can be sold both as part of systems and as individual after sales items. Besides, there is uncertainty in determining replenishment lead times. An optimization problem is formulated considering customer satisfaction, inventory holding costs and correlation between demands. To ascertain its validity, a discrete event simulation is executed over historical demand. Simulation also helps to check the solution robustness by executing the proposed inventory levels over statistically generated demands. The result provides the right mix of finished products which should be stored on the shelves. 90% reduction in lost sales and 35% in inventory value on hand have been projected. The results have been further implemented at the part level inventory.by Samarth Chugh.M.Eng