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Stanford Aging and Ethnogeriatrics Research Center (SAGE Center)

Stanford Aging and Ethnogeriatrics Research Center (SAGE Center)

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SAGE awardees

Iván Mejía-Guevara, Ph.D.

Sr. Research Scientist
Department of Biology, Stanford Center for Population Health Sciences

https://profiles.stanford.edu/ivan-mejia-guevara

Dr. Iván Mejía received his Ph.D. in Computer Science from the National Autonomous University of Mexico (UNAM). He received postdoctoral training and served as Associate Specialist in the Department of Demography at UC Berkeley. Before coming to Stanford, he was Research Associate at the Harvard Center for Population and Development Studies and served as a consultant to the United Nations Economic Commission for Latin America and the Caribbean. His primary interests are statistical modeling for population health research; understanding the social determinants of health and mortality of minority populations, population aging and intergenerational transfers; lifespan inequality, child mortality, and maternal and child health in developing countries.

SAGE Project: Minority health and aging: how do age and location interact with other factors obtained from emerging technologies?

Paradoxically, life expectancy of Hispanics living in the U.S. is higher on average than non-Hispanic whites, but unrepresented minority groups are aging at different rates and they are not evenly distributed across the U.S. territory. The goal of this project is to investigate the sources of spatial variation of health outcomes, mortality, and aging of minority populations. I will use geospatial data from emerging technologies and statistical learning techniques well-suited for prediction, while also exploring compositional and causal effects.

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    Next-Generation Artificial Intelligence for Diagnosis: From Predicting Diagnostic Labels to "Wayfinding"

    Julia Adler-Milstein, PhD1; Jonathan H. Chen, MD, PhD; Gurpreet Dhaliwal, MD

    Improving the diagnostic process is a quality and safety priority.With the digitization of health records and rapid expansion of health data, the cognitive demand on the diagnostician has increased. The use of artificial intelligence (AI) to assist human cognition has the potential to reduce this demand and associated diagnostic errors. However, current AI tools have not realized this potential, due in part to the long-standing focus of these tools on predicting final diagnostic labels instead of helping clinicians navigate the dynamic refinement process of diagnosis. This Viewpoint highlights the importance of shifting the role of diagnostic AI from predicting labels to “wayfinding” (interpreting context and providing cues that guide the diagnostician).