MortalityMinder: Visualization and AI Interpretations of Social Determinants of Premature Mortality in the United States

Autor: Karan Bhanot, John S. Erickson, Kristin P. Bennett
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Information, Vol 15, Iss 5, p 254 (2024)
Druh dokumentu: article
ISSN: 15050254
2078-2489
DOI: 10.3390/info15050254
Popis: MortalityMinder enables healthcare researchers, providers, payers, and policy makers to gain actionable insights into where and why premature mortality rates due to all causes, cancer, cardiovascular disease, and deaths of despair rose between 2000 and 2017 for adults aged 25–64. MortalityMinder is designed as an open-source web-based visualization tool that enables interactive analysis and exploration of social, economic, and geographic factors associated with mortality at the county level. We provide case studies to illustrate how MortalityMinder finds interesting relationships between health determinants and deaths of despair. We also demonstrate how GPT-4 can help translate statistical results from MortalityMinder into actionable insights to improve population health. When combined with MortalityMinder results, GPT-4 provides hypotheses on why socio-economic risk factors are associated with mortality, how they might be causal, and what actions could be taken related to the risk factors to improve outcomes with supporting citations. We find that GPT-4 provided plausible and insightful answers about the relationship between social determinants and mortality. Our work is a first step towards enabling public health stakeholders to automatically discover and visualize relationships between social determinants of health and mortality based on available data and explain and transform these into meaningful results using artificial intelligence.
Databáze: Directory of Open Access Journals
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