Newswise – Los Angeles (July 30, 2025) – Two new studies from the Department of Computational Biomedicine at Cedars-Sinai advance what they know about improving medical and medical research using machine learning and big data. Both studies were published in peer-reviewed journals pattern.
In the first study, Cedars-Sinai investigators applied advanced statistical techniques to analyze electronic health records from nearly 100,000 hospital stays. This approach identified drugs that were unexpectedly associated with an increase or decrease in blood glucose levels in hospitalized patients.
“Our findings provide practical insights that will help clinicians predict and manage drug-related glucose changes and ultimately improve glucose safety for hospital patients,” said Dr. Jesse G. Meyer, assistant professor of computational biomedical practices at Cedars-Sinai and author of the study.
In the second study co-led by Cedars-Sinai, investigators developed a safe method to pool patient data from multiple hospitals for their research. This method allows hospitals to send statistical summary of patient characteristics rather than personal medical data to a central location for analysis by investigators, reducing the risk of careless disclosure of sensitive patient information.
“Our innovative approach opens the door to larger and diverse research that better protects patient privacy, improves research quality, and supports the development of more effective treatments,” says Dr. Ruowang Li, assistant professor of computational biomedicine at Cedars-Sinai and co-author of the study.
“Both studies highlight our unique approach to using machine learning and big data in academic medicine,” says Dr. Jason Moore, professor at Cedars-Sinai's School of Computational Biomedical Sciences and co-author of the study. “These studies will encourage collaboration and ultimately lead to patient care and research driven by data, overcoming the outcome gaps and creating healthier lives.”
First study:
Other authors of Cedar Sinai include Amanda Momenzadeh, Medicine, Caleb Krany, MS, Mississippi, Son Choi, MS, Katherine Brissy, MS, Mourado Tibiouart, PhD, Rome Janchandani, Rome Janchandani, MD, Joshua Pebnick, MD, MSHS, and Jason H. More, PHD..
Acknowledgements: The authors thank Edward Kowalewski, Kevin Japardi, and Honest Enterprise Research Broker of EHR Data Extraction Services. This study was supported by the NIH National Center for Advance Translational Science (NCATS), UCLA CTSI grant number UL1TR001881.
Declaration of Interest: Amanda Momenzadeh and Jesse G. Meyer have provisional patents related to this work. Jason H. Moore is a member of the Patterns Advisory Board.
Second survey:
Another author of Cedar Sinai was Jason H. Moore. Other authors include Luke Benz, Louis Duan, Joshua C. Denny, Hacon Haconerson, Jonathan D. Mosley, Jordan W. Smoller, Wei Wei, Thomas Lumley, Marilyn D. Richie, and Yong Chen (co-compatible authors).
understand: Funding sources include NIH R01 LM010098, AG066833, GM148494, LM014344, LM012607, LM013519, AI130460, AG073435, RF1AG077820, R56AG069880, R56AG074604 U01TR003709, R21AI167418 and R21EY034179. MDR was funded by R01HG010067 and R01HL169458.
Emerge Network (Phase III). This phase of the Emerge network was initiated and funded by NHGRI through the following grants: U01HG8657 (Group Health Cooperative/University of Washington). U01HG8685 (Brigham and Women's Hospital); U01HG8672 (Vanderbilt University Medical Center); U01HG8666 (Cincinnati Children's Hospital Medical Center); U01HG6379 (Mayo Clinic); U01HG8679 (Geisinger Clinic); U01HG8680 (Columbia University Health Sciences); U01HG8684 (Philadelphia Children's Hospital); U01HG8673 (Northwestern University); U01HG8701 (Coordinated Center by Vanderbilt University Medical Center); U01HG8676 (Partners Healthcare/Broad Institute); and U01HG8664 (Baylor College of Medicine).
British biobank. All data in this cohort were related to Project 32133 – “Integration of multiorgan imaging phenotype, clinical phenotype, and genomic data.”
Declaration of Interest: Jason H. Moore will serve as current member pattern Advisory Committee. Other authors report no competing benefits.
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