Title: Learning to Address Health Inequality in the United States with a Bayesian Decision Network,
Abstract: 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.