Summer Students Tackle COVID-19
With near-daily changes in the understanding of SARS-CoV-2 and how COVID-19 is transmitted and spread, researchers are challenged with mountains of data in the race to end the outbreak. Adding to that, the inaccessibility of high-quality electronic health records (EHRs) creates a challenge for researchers needing more complete information to map the viral outbreak and fully understand its broader impact on health.
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As a part of the Computational Research Division’s (CRD) summer student program at Lawrence Berkeley National Laboratory, four graduate students from the University of California, Davis (UC Davis) researched a method that could allow doctors and researchers to leverage valuable health information in the battle against COVID-19 while also preserving patient privacy in COVID-19-related EHRs. The students worked to support the COVID-19 response by using actual EHR data and applying differential privacy, a data-driven approach first published in 2006 that provides strong, statistical privacy guarantees while balancing privacy and the utility of data for use in machine learning and other analyses. Differential privacy is widely used by companies like Apple, Google, and Microsoft, as well as the U.S. Census Bureau and the United Nations, among others.
“Earlier this year, many researchers were sitting on the sidelines of the COVID-19 response wondering how we could make a difference,” said Sean Peisert, research lead and staff scientist in the Data Science and Technology Department in CRD. Peisert and Nicholas Anderson, professor of Informatics and director of Informatics Research at UC Davis Health, combined forces and determined that applying privacy techniques to actual EHR data was one way to contribute to the research on the pandemic.