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e-HAIL Event

Learning Digital Twin Representations of Patients from Electric Health Records

Geoffrey Siwo, Ph.D.Research Assistant Professor of Learning Health SciencesU-M Medical SchoolTiffany Tang, Ph.D.Postdoctoral Research Fellow in StatisticsU-M College of Literature, Science, and the Arts

Digital twins are virtual representations of physical systems that mimic the structure, context and behavior of their physical counterparts.  In biomedical research, digital twins span multiple scales, for example, the molecular dynamics of single biomolecules, the temporal and spatial biomolecular interactions within single cells, cell-cell interactions, and whole body simulations. Digital twin technologies could have a transformative impact on medicine in areas such as drug discovery, clinical trials, personalized medicine and health systems operations. In this session, we will explore the possibility of integrating electronic health records with high-throughput biological datasets and fundamental biological knowledge to inform the design of digital twin representations of health states. We will also discuss some key challenges and opportunities emerging from a recent National Academies of Sciences and Engineering (NASEM) workshop urging multi-agency action to support the development of digital twins across engineering and sciences.

Zoom information will be sent to e-HAIL members.


J. Henrike Florusbosch