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Our Mission

DBMI was founded in the summer of 2009; since then, it has been a Division within the Department of Medicine in the School of Medicine and, in 2015, it also became a Department of UC San Diego Health -- having the tri-partite mission of research, training, and service/collaboration.

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nurses and physicians wearing scrubs with all of us logo standing in front of ACTRI building

All of Us Research Program

The mission is simple. We want to speed up health research breakthroughs. To do this, we're asking one million people to share information about their health, habits, and what it's like where they live. By looking for patterns in this information, researchers may learn more about what affects people's health.

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Featured Projects

New Research Center Brings Genomic Medicine to Individuals of Admixed Ancestry

Researchers at UC San Diego School of Medicine awarded $11.7 million by National Institutes of Health to identify genomic and socioeconomic factors contributing to health and disease in admixed individuals

About the project

COVID-19 Data Discovery from Clinical Records

COVID-19 Data Discovery from Clinical Records is your resource for questions and answers about COVID-19, funded by the Gordon and Betty Moore Foundation! We use electronic health record (EHR) data from 12 leading medical centers to answer simple and complex clinical questions related to COVID-19.

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Featured Papers & Books

Fatemeh Amrollahi, Supreeth P Shashikumar, Angela Meier, Lucila Ohno-Machado, Shamim Nemati, Gabriel Wardi, All of Us Data Helps Better Predict Hospital Readmission for Patients With Sepsis, National Institue of Health All of Us Research Program, October 4, 2022

Fatemeh Amrollahi, Supreeth P Shashikumar, Angela Meier, Lucila Ohno-Machado, Shamim Nemati, Gabriel Wardi, Inclusion of social determinants of health improves sepsis readmission prediction modelsJournal of the American Medical Informatics Association, Volume 29, Issue 7, July 2022, Pages 1263–1270

Transforming Biomedical Informatics and Health Information Access: Don Lindberg and the U.S. National Library of Medicine, 978-1-64368-238-9 (print) | 978-1-64368-239-6 (online)

Shashikumar, S.P., Wardi, G., Malhotra, A. et al. Artificial intelligence sepsis prediction algorithm learns to say “I don’t know”. npj Digit. Med. 4, 134 (2021).

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Featured Classes

MED 264

Principles of Biomedical Informatics

About the Course

MED 262

Current Trends in Biomedical Informatics

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Cancer Genomics Journal Club

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Sponsorship Opportunities

Your generous gifts enable us to provide valuable academic and research resources and opportunities for biomedical informatics students and faculty.

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