Clinical, Standardization & Algorithmic Pathways to Automating Discovery with EHR Data -Charles Mayo

“Clinical, Standardization and Algorithmic Pathways to Automating Discovery with EHR Data”
Presentation abstract: In Radiation Oncology we have constructed a learning health system that combines an infrastructure for automated, standards driven aggregation, integration and harmonization of electronic health records (EHR) data for all treated patients. We then developed automated statistical and artificial intelligence algorithms to distill large scale, comprehensive data to discover prognostic features for clinical outcomes. The combination of standardizations, data centric clinical practices, and automation is giving use a pathway to more evidence driven discovery.
Charles Mayo, Professor, Director of Radiation Oncology Informatics and Analytics, University of Michigan
About this event: Significant advancements in Artificial Intelligence (AI), including Generative AI, and the hardware and software research environment are enabling researchers to develop AI-driven research workflows (ARWs): AI and Generative AI for hypothesis generation, experimental design and monitoring, as well as data acquisition, processing and analytics. Such ARWs will not only significantly accelerate research, but also enable new research possibilities.
This mini-symposium featured a keynote by Ian Foster (Argonne National Lab) and presentations from University of Michigan faculty members showcasing how they have developed and embedded AI-driven components in their research workflows.
For more information about the series and speakers, please visit the event page at: midas.umich.edu/ai-driven-res...
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